Find, fix and prevent vulnerabilities in your code.
critical severity
- Vulnerable module: handlebars
- Introduced through: handlebars@4.0.14
Detailed paths
-
Introduced through: hapi-rethinkdb-dash@fs-opensource/hapi-rethinkdb-dash#bde4f9de489cd0cdc2a4ebfa78de82a44b15e1f9 › handlebars@4.0.14Remediation: Upgrade to handlebars@4.5.3.
Overview
handlebars is an extension to the Mustache templating language.
Affected versions of this package are vulnerable to Prototype Pollution. It is possible to add or modify properties to the Object prototype through a malicious template. This may allow attackers to crash the application or execute Arbitrary Code in specific conditions.
Details
Prototype Pollution is a vulnerability affecting JavaScript. Prototype Pollution refers to the ability to inject properties into existing JavaScript language construct prototypes, such as objects. JavaScript allows all Object attributes to be altered, including their magical attributes such as _proto_
, constructor
and prototype
. An attacker manipulates these attributes to overwrite, or pollute, a JavaScript application object prototype of the base object by injecting other values. Properties on the Object.prototype
are then inherited by all the JavaScript objects through the prototype chain. When that happens, this leads to either denial of service by triggering JavaScript exceptions, or it tampers with the application source code to force the code path that the attacker injects, thereby leading to remote code execution.
There are two main ways in which the pollution of prototypes occurs:
Unsafe
Object
recursive mergeProperty definition by path
Unsafe Object recursive merge
The logic of a vulnerable recursive merge function follows the following high-level model:
merge (target, source)
foreach property of source
if property exists and is an object on both the target and the source
merge(target[property], source[property])
else
target[property] = source[property]
When the source object contains a property named _proto_
defined with Object.defineProperty()
, the condition that checks if the property exists and is an object on both the target and the source passes and the merge recurses with the target, being the prototype of Object
and the source of Object
as defined by the attacker. Properties are then copied on the Object
prototype.
Clone operations are a special sub-class of unsafe recursive merges, which occur when a recursive merge is conducted on an empty object: merge({},source)
.
lodash
and Hoek
are examples of libraries susceptible to recursive merge attacks.
Property definition by path
There are a few JavaScript libraries that use an API to define property values on an object based on a given path. The function that is generally affected contains this signature: theFunction(object, path, value)
If the attacker can control the value of “path”, they can set this value to _proto_.myValue
. myValue
is then assigned to the prototype of the class of the object.
Types of attacks
There are a few methods by which Prototype Pollution can be manipulated:
Type | Origin | Short description |
---|---|---|
Denial of service (DoS) | Client | This is the most likely attack. DoS occurs when Object holds generic functions that are implicitly called for various operations (for example, toString and valueOf ). The attacker pollutes Object.prototype.someattr and alters its state to an unexpected value such as Int or Object . In this case, the code fails and is likely to cause a denial of service. For example: if an attacker pollutes Object.prototype.toString by defining it as an integer, if the codebase at any point was reliant on someobject.toString() it would fail. |
Remote Code Execution | Client | Remote code execution is generally only possible in cases where the codebase evaluates a specific attribute of an object, and then executes that evaluation. For example: eval(someobject.someattr) . In this case, if the attacker pollutes Object.prototype.someattr they are likely to be able to leverage this in order to execute code. |
Property Injection | Client | The attacker pollutes properties that the codebase relies on for their informative value, including security properties such as cookies or tokens. For example: if a codebase checks privileges for someuser.isAdmin , then when the attacker pollutes Object.prototype.isAdmin and sets it to equal true , they can then achieve admin privileges. |
Affected environments
The following environments are susceptible to a Prototype Pollution attack:
Application server
Web server
How to prevent
Freeze the prototype— use
Object.freeze (Object.prototype)
.Require schema validation of JSON input.
Avoid using unsafe recursive merge functions.
Consider using objects without prototypes (for example,
Object.create(null)
), breaking the prototype chain and preventing pollution.As a best practice use
Map
instead ofObject
.
For more information on this vulnerability type:
Arteau, Oliver. “JavaScript prototype pollution attack in NodeJS application.” GitHub, 26 May 2018
Remediation
Upgrade handlebars
to version 3.0.8, 4.5.3 or higher.
References
high severity
- Vulnerable module: nodemailer
- Introduced through: nodemailer@3.1.5
Detailed paths
-
Introduced through: hapi-rethinkdb-dash@fs-opensource/hapi-rethinkdb-dash#bde4f9de489cd0cdc2a4ebfa78de82a44b15e1f9 › nodemailer@3.1.5Remediation: Upgrade to nodemailer@6.4.16.
Overview
nodemailer is an Easy as cake e-mail sending from your Node.js applications
Affected versions of this package are vulnerable to Command Injection. Use of crafted recipient email addresses may result in arbitrary command flag injection in sendmail transport for sending mails.
PoC
-bi@example.com (-bi Initialize the alias database.)
-d0.1a@example.com (The option -d0.1 prints the version of sendmail and the options it was compiled with.)
-Dfilename@example.com (Debug output ffile)
Remediation
Upgrade nodemailer
to version 6.4.16 or higher.
References
high severity
- Vulnerable module: tar
- Introduced through: bcrypt@1.0.3
Detailed paths
-
Introduced through: hapi-rethinkdb-dash@fs-opensource/hapi-rethinkdb-dash#bde4f9de489cd0cdc2a4ebfa78de82a44b15e1f9 › bcrypt@1.0.3 › node-pre-gyp@0.6.36 › tar@2.2.2Remediation: Upgrade to bcrypt@2.0.0.
-
Introduced through: hapi-rethinkdb-dash@fs-opensource/hapi-rethinkdb-dash#bde4f9de489cd0cdc2a4ebfa78de82a44b15e1f9 › bcrypt@1.0.3 › node-pre-gyp@0.6.36 › tar-pack@3.4.1 › tar@2.2.2
Overview
tar is a full-featured Tar for Node.js.
Affected versions of this package are vulnerable to Arbitrary File Write. node-tar
aims to guarantee that any file whose location would be modified by a symbolic link is not extracted. This is, in part, achieved by ensuring that extracted directories are not symlinks. Additionally, in order to prevent unnecessary stat calls to determine whether a given path is a directory, paths are cached when directories are created.
This logic was insufficient when extracting tar
files that contained both a directory and a symlink with the same name as the directory, where the symlink and directory names in the archive entry used backslashes as a path separator on posix systems. The cache checking logic used both \
and /
characters as path separators. However, \
is a valid filename character on posix systems.
By first creating a directory, and then replacing that directory with a symlink, it is possible to bypass node-tar
symlink checks on directories, essentially allowing an untrusted tar
file to symlink into an arbitrary location. This can lead to extracting arbitrary files into that location, thus allowing arbitrary file creation and overwrite.
Additionally, a similar confusion could arise on case-insensitive filesystems. If a tar
archive contained a directory at FOO
, followed by a symbolic link named foo
, then on case-insensitive file systems, the creation of the symbolic link would remove the directory from the filesystem, but not from the internal directory cache, as it would not be treated as a cache hit. A subsequent file entry within the FOO
directory would then be placed in the target of the symbolic link, thinking that the directory had already been created.
Remediation
Upgrade tar
to version 6.1.7, 5.0.8, 4.4.16 or higher.
References
high severity
- Vulnerable module: tar
- Introduced through: bcrypt@1.0.3
Detailed paths
-
Introduced through: hapi-rethinkdb-dash@fs-opensource/hapi-rethinkdb-dash#bde4f9de489cd0cdc2a4ebfa78de82a44b15e1f9 › bcrypt@1.0.3 › node-pre-gyp@0.6.36 › tar@2.2.2Remediation: Upgrade to bcrypt@2.0.0.
-
Introduced through: hapi-rethinkdb-dash@fs-opensource/hapi-rethinkdb-dash#bde4f9de489cd0cdc2a4ebfa78de82a44b15e1f9 › bcrypt@1.0.3 › node-pre-gyp@0.6.36 › tar-pack@3.4.1 › tar@2.2.2
Overview
tar is a full-featured Tar for Node.js.
Affected versions of this package are vulnerable to Arbitrary File Write. node-tar
aims to guarantee that any file whose location would be modified by a symbolic link is not extracted. This is, in part, achieved by ensuring that extracted directories are not symlinks. Additionally, in order to prevent unnecessary stat calls to determine whether a given path is a directory, paths are cached when directories are created.
This logic is insufficient when extracting tar
files that contain two directories and a symlink with names containing unicode values that normalized to the same value. Additionally, on Windows systems, long path portions would resolve to the same file system entities as their 8.3 "short path" counterparts.
A specially crafted tar
archive can include directories with two forms of the path that resolve to the same file system entity, followed by a symbolic link with a name in the first form, lastly followed by a file using the second form. This leads to bypassing node-tar
symlink checks on directories, essentially allowing an untrusted tar
file to symlink into an arbitrary location and extracting arbitrary files into that location.
Remediation
Upgrade tar
to version 6.1.9, 5.0.10, 4.4.18 or higher.
References
high severity
- Vulnerable module: tar
- Introduced through: bcrypt@1.0.3
Detailed paths
-
Introduced through: hapi-rethinkdb-dash@fs-opensource/hapi-rethinkdb-dash#bde4f9de489cd0cdc2a4ebfa78de82a44b15e1f9 › bcrypt@1.0.3 › node-pre-gyp@0.6.36 › tar@2.2.2Remediation: Upgrade to bcrypt@2.0.0.
-
Introduced through: hapi-rethinkdb-dash@fs-opensource/hapi-rethinkdb-dash#bde4f9de489cd0cdc2a4ebfa78de82a44b15e1f9 › bcrypt@1.0.3 › node-pre-gyp@0.6.36 › tar-pack@3.4.1 › tar@2.2.2
Overview
tar is a full-featured Tar for Node.js.
Affected versions of this package are vulnerable to Arbitrary File Write. node-tar
aims to guarantee that any file whose location would be outside of the extraction target directory is not extracted. This is, in part, accomplished by sanitizing absolute paths of entries within the archive, skipping archive entries that contain ..
path portions, and resolving the sanitized paths against the extraction target directory.
This logic is insufficient on Windows systems when extracting tar
files that contain a path that is not an absolute path, but specify a drive letter different from the extraction target, such as C:some\path
. If the drive letter does not match the extraction target, for example D:\extraction\dir
, then the result of path.resolve(extractionDirectory, entryPath)
resolves against the current working directory on the C:
drive, rather than the extraction target directory.
Additionally, a ..
portion of the path can occur immediately after the drive letter, such as C:../foo
, and is not properly sanitized by the logic that checks for ..
within the normalized and split portions of the path.
Note: This only affects users of node-tar
on Windows systems.
Remediation
Upgrade tar
to version 6.1.9, 5.0.10, 4.4.18 or higher.
References
high severity
- Vulnerable module: tar
- Introduced through: bcrypt@1.0.3
Detailed paths
-
Introduced through: hapi-rethinkdb-dash@fs-opensource/hapi-rethinkdb-dash#bde4f9de489cd0cdc2a4ebfa78de82a44b15e1f9 › bcrypt@1.0.3 › node-pre-gyp@0.6.36 › tar@2.2.2Remediation: Upgrade to bcrypt@2.0.0.
-
Introduced through: hapi-rethinkdb-dash@fs-opensource/hapi-rethinkdb-dash#bde4f9de489cd0cdc2a4ebfa78de82a44b15e1f9 › bcrypt@1.0.3 › node-pre-gyp@0.6.36 › tar-pack@3.4.1 › tar@2.2.2
Overview
tar is a full-featured Tar for Node.js.
Affected versions of this package are vulnerable to Arbitrary File Overwrite. This is due to insufficient symlink protection.
node-tar
aims to guarantee that any file whose location would be modified by a symbolic link is not extracted. This is, in part, achieved by ensuring that extracted directories are not symlinks. Additionally, in order to prevent unnecessary stat
calls to determine whether a given path is a directory, paths are cached when directories are created.
This logic is insufficient when extracting tar files that contain both a directory and a symlink with the same name as the directory. This order of operations results in the directory being created and added to the node-tar
directory cache. When a directory is present in the directory cache, subsequent calls to mkdir
for that directory are skipped.
However, this is also where node-tar
checks for symlinks occur. By first creating a directory, and then replacing that directory with a symlink, it is possible to bypass node-tar
symlink checks on directories, essentially allowing an untrusted tar file to symlink into an arbitrary location and subsequently extracting arbitrary files into that location.
Remediation
Upgrade tar
to version 3.2.3, 4.4.15, 5.0.7, 6.1.2 or higher.
References
high severity
- Vulnerable module: tar
- Introduced through: bcrypt@1.0.3
Detailed paths
-
Introduced through: hapi-rethinkdb-dash@fs-opensource/hapi-rethinkdb-dash#bde4f9de489cd0cdc2a4ebfa78de82a44b15e1f9 › bcrypt@1.0.3 › node-pre-gyp@0.6.36 › tar@2.2.2Remediation: Upgrade to bcrypt@2.0.0.
-
Introduced through: hapi-rethinkdb-dash@fs-opensource/hapi-rethinkdb-dash#bde4f9de489cd0cdc2a4ebfa78de82a44b15e1f9 › bcrypt@1.0.3 › node-pre-gyp@0.6.36 › tar-pack@3.4.1 › tar@2.2.2
Overview
tar is a full-featured Tar for Node.js.
Affected versions of this package are vulnerable to Arbitrary File Overwrite. This is due to insufficient absolute path sanitization.
node-tar
aims to prevent extraction of absolute file paths by turning absolute paths into relative paths when the preservePaths
flag is not set to true
. This is achieved by stripping the absolute path root from any absolute file paths contained in a tar file. For example, the path /home/user/.bashrc
would turn into home/user/.bashrc
.
This logic is insufficient when file paths contain repeated path roots such as ////home/user/.bashrc
. node-tar
only strips a single path root from such paths. When given an absolute file path with repeating path roots, the resulting path (e.g. ///home/user/.bashrc
) still resolves to an absolute path.
Remediation
Upgrade tar
to version 3.2.2, 4.4.14, 5.0.6, 6.1.1 or higher.
References
high severity
- Vulnerable module: handlebars
- Introduced through: handlebars@4.0.14
Detailed paths
-
Introduced through: hapi-rethinkdb-dash@fs-opensource/hapi-rethinkdb-dash#bde4f9de489cd0cdc2a4ebfa78de82a44b15e1f9 › handlebars@4.0.14Remediation: Upgrade to handlebars@4.5.3.
Overview
handlebars is an extension to the Mustache templating language.
Affected versions of this package are vulnerable to Arbitrary Code Execution. The package's lookup helper doesn't validate templates correctly, allowing attackers to submit templates that execute arbitrary JavaScript in the system.
PoC
{{#with split as |a|}}
{{pop (push "alert('Vulnerable Handlebars JS');")}}
{{#with (concat (lookup join (slice 0 1)))}}
{{#each (slice 2 3)}}
{{#with (apply 0 a)}}
{{.}}
{{/with}}
{{/each}}
{{/with}}
{{/with}}
{{/with}}
Remediation
Upgrade handlebars
to version 3.0.8, 4.5.3 or higher.
References
high severity
- Vulnerable module: ammo
- Introduced through: hapi@16.1.1 and inert@4.1.0
Detailed paths
-
Introduced through: hapi-rethinkdb-dash@fs-opensource/hapi-rethinkdb-dash#bde4f9de489cd0cdc2a4ebfa78de82a44b15e1f9 › hapi@16.1.1 › ammo@2.1.2
-
Introduced through: hapi-rethinkdb-dash@fs-opensource/hapi-rethinkdb-dash#bde4f9de489cd0cdc2a4ebfa78de82a44b15e1f9 › inert@4.1.0 › ammo@2.1.2
Overview
ammo is a HTTP Range processing utilities.
Note This package is deprecated and is now maintained as @hapi/ammo
.
Affected versions of this package are vulnerable to Denial of Service (DoS). The Range HTTP header parser has a vulnerability which will cause the function to throw a system error if the header is set to an invalid value. Because hapi is not expecting the function to ever throw, the error is thrown all the way up the stack. If no unhandled exception handler is available, the application will exist, allowing an attacker to shut down services.
Details
Denial of Service (DoS) describes a family of attacks, all aimed at making a system inaccessible to its original and legitimate users. There are many types of DoS attacks, ranging from trying to clog the network pipes to the system by generating a large volume of traffic from many machines (a Distributed Denial of Service - DDoS - attack) to sending crafted requests that cause a system to crash or take a disproportional amount of time to process.
The Regular expression Denial of Service (ReDoS) is a type of Denial of Service attack. Regular expressions are incredibly powerful, but they aren't very intuitive and can ultimately end up making it easy for attackers to take your site down.
Let’s take the following regular expression as an example:
regex = /A(B|C+)+D/
This regular expression accomplishes the following:
A
The string must start with the letter 'A'(B|C+)+
The string must then follow the letter A with either the letter 'B' or some number of occurrences of the letter 'C' (the+
matches one or more times). The+
at the end of this section states that we can look for one or more matches of this section.D
Finally, we ensure this section of the string ends with a 'D'
The expression would match inputs such as ABBD
, ABCCCCD
, ABCBCCCD
and ACCCCCD
It most cases, it doesn't take very long for a regex engine to find a match:
$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCD")'
0.04s user 0.01s system 95% cpu 0.052 total
$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCX")'
1.79s user 0.02s system 99% cpu 1.812 total
The entire process of testing it against a 30 characters long string takes around ~52ms. But when given an invalid string, it takes nearly two seconds to complete the test, over ten times as long as it took to test a valid string. The dramatic difference is due to the way regular expressions get evaluated.
Most Regex engines will work very similarly (with minor differences). The engine will match the first possible way to accept the current character and proceed to the next one. If it then fails to match the next one, it will backtrack and see if there was another way to digest the previous character. If it goes too far down the rabbit hole only to find out the string doesn’t match in the end, and if many characters have multiple valid regex paths, the number of backtracking steps can become very large, resulting in what is known as catastrophic backtracking.
Let's look at how our expression runs into this problem, using a shorter string: "ACCCX". While it seems fairly straightforward, there are still four different ways that the engine could match those three C's:
- CCC
- CC+C
- C+CC
- C+C+C.
The engine has to try each of those combinations to see if any of them potentially match against the expression. When you combine that with the other steps the engine must take, we can use RegEx 101 debugger to see the engine has to take a total of 38 steps before it can determine the string doesn't match.
From there, the number of steps the engine must use to validate a string just continues to grow.
String | Number of C's | Number of steps |
---|---|---|
ACCCX | 3 | 38 |
ACCCCX | 4 | 71 |
ACCCCCX | 5 | 136 |
ACCCCCCCCCCCCCCX | 14 | 65,553 |
By the time the string includes 14 C's, the engine has to take over 65,000 steps just to see if the string is valid. These extreme situations can cause them to work very slowly (exponentially related to input size, as shown above), allowing an attacker to exploit this and can cause the service to excessively consume CPU, resulting in a Denial of Service.
Remediation
There is no fixed version for ammo
.
References
high severity
- Vulnerable module: ansi-regex
- Introduced through: bcrypt@1.0.3
Detailed paths
-
Introduced through: hapi-rethinkdb-dash@fs-opensource/hapi-rethinkdb-dash#bde4f9de489cd0cdc2a4ebfa78de82a44b15e1f9 › bcrypt@1.0.3 › node-pre-gyp@0.6.36 › npmlog@4.1.2 › gauge@2.7.4 › strip-ansi@3.0.1 › ansi-regex@2.1.1
-
Introduced through: hapi-rethinkdb-dash@fs-opensource/hapi-rethinkdb-dash#bde4f9de489cd0cdc2a4ebfa78de82a44b15e1f9 › bcrypt@1.0.3 › node-pre-gyp@0.6.36 › npmlog@4.1.2 › gauge@2.7.4 › string-width@1.0.2 › strip-ansi@3.0.1 › ansi-regex@2.1.1
Overview
Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS) due to the sub-patterns [[\\]()#;?]*
and (?:;[-a-zA-Z\\d\\/#&.:=?%@~_]*)*
.
PoC
import ansiRegex from 'ansi-regex';
for(var i = 1; i <= 50000; i++) {
var time = Date.now();
var attack_str = "\u001B["+";".repeat(i*10000);
ansiRegex().test(attack_str)
var time_cost = Date.now() - time;
console.log("attack_str.length: " + attack_str.length + ": " + time_cost+" ms")
}
Details
Denial of Service (DoS) describes a family of attacks, all aimed at making a system inaccessible to its original and legitimate users. There are many types of DoS attacks, ranging from trying to clog the network pipes to the system by generating a large volume of traffic from many machines (a Distributed Denial of Service - DDoS - attack) to sending crafted requests that cause a system to crash or take a disproportional amount of time to process.
The Regular expression Denial of Service (ReDoS) is a type of Denial of Service attack. Regular expressions are incredibly powerful, but they aren't very intuitive and can ultimately end up making it easy for attackers to take your site down.
Let’s take the following regular expression as an example:
regex = /A(B|C+)+D/
This regular expression accomplishes the following:
A
The string must start with the letter 'A'(B|C+)+
The string must then follow the letter A with either the letter 'B' or some number of occurrences of the letter 'C' (the+
matches one or more times). The+
at the end of this section states that we can look for one or more matches of this section.D
Finally, we ensure this section of the string ends with a 'D'
The expression would match inputs such as ABBD
, ABCCCCD
, ABCBCCCD
and ACCCCCD
It most cases, it doesn't take very long for a regex engine to find a match:
$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCD")'
0.04s user 0.01s system 95% cpu 0.052 total
$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCX")'
1.79s user 0.02s system 99% cpu 1.812 total
The entire process of testing it against a 30 characters long string takes around ~52ms. But when given an invalid string, it takes nearly two seconds to complete the test, over ten times as long as it took to test a valid string. The dramatic difference is due to the way regular expressions get evaluated.
Most Regex engines will work very similarly (with minor differences). The engine will match the first possible way to accept the current character and proceed to the next one. If it then fails to match the next one, it will backtrack and see if there was another way to digest the previous character. If it goes too far down the rabbit hole only to find out the string doesn’t match in the end, and if many characters have multiple valid regex paths, the number of backtracking steps can become very large, resulting in what is known as catastrophic backtracking.
Let's look at how our expression runs into this problem, using a shorter string: "ACCCX". While it seems fairly straightforward, there are still four different ways that the engine could match those three C's:
- CCC
- CC+C
- C+CC
- C+C+C.
The engine has to try each of those combinations to see if any of them potentially match against the expression. When you combine that with the other steps the engine must take, we can use RegEx 101 debugger to see the engine has to take a total of 38 steps before it can determine the string doesn't match.
From there, the number of steps the engine must use to validate a string just continues to grow.
String | Number of C's | Number of steps |
---|---|---|
ACCCX | 3 | 38 |
ACCCCX | 4 | 71 |
ACCCCCX | 5 | 136 |
ACCCCCCCCCCCCCCX | 14 | 65,553 |
By the time the string includes 14 C's, the engine has to take over 65,000 steps just to see if the string is valid. These extreme situations can cause them to work very slowly (exponentially related to input size, as shown above), allowing an attacker to exploit this and can cause the service to excessively consume CPU, resulting in a Denial of Service.
Remediation
Upgrade ansi-regex
to version 3.0.1, 4.1.1, 5.0.1, 6.0.1 or higher.
References
high severity
- Vulnerable module: bcrypt
- Introduced through: bcrypt@1.0.3
Detailed paths
-
Introduced through: hapi-rethinkdb-dash@fs-opensource/hapi-rethinkdb-dash#bde4f9de489cd0cdc2a4ebfa78de82a44b15e1f9 › bcrypt@1.0.3Remediation: Upgrade to bcrypt@5.0.0.
Overview
bcrypt is an A library to help you hash passwords.
Affected versions of this package are vulnerable to Insecure Encryption. Data is truncated wrong when its length is greater than 255 bytes.
Remediation
Upgrade bcrypt
to version 5.0.0 or higher.
References
high severity
- Vulnerable module: handlebars
- Introduced through: handlebars@4.0.14
Detailed paths
-
Introduced through: hapi-rethinkdb-dash@fs-opensource/hapi-rethinkdb-dash#bde4f9de489cd0cdc2a4ebfa78de82a44b15e1f9 › handlebars@4.0.14Remediation: Upgrade to handlebars@4.4.5.
Overview
handlebars is an extension to the Mustache templating language.
Affected versions of this package are vulnerable to Denial of Service (DoS). The package's parser may be forced into an endless loop while processing specially-crafted templates, which may allow attackers to exhaust system resources leading to Denial of Service.
Details
Denial of Service (DoS) describes a family of attacks, all aimed at making a system inaccessible to its original and legitimate users. There are many types of DoS attacks, ranging from trying to clog the network pipes to the system by generating a large volume of traffic from many machines (a Distributed Denial of Service - DDoS - attack) to sending crafted requests that cause a system to crash or take a disproportional amount of time to process.
The Regular expression Denial of Service (ReDoS) is a type of Denial of Service attack. Regular expressions are incredibly powerful, but they aren't very intuitive and can ultimately end up making it easy for attackers to take your site down.
Let’s take the following regular expression as an example:
regex = /A(B|C+)+D/
This regular expression accomplishes the following:
A
The string must start with the letter 'A'(B|C+)+
The string must then follow the letter A with either the letter 'B' or some number of occurrences of the letter 'C' (the+
matches one or more times). The+
at the end of this section states that we can look for one or more matches of this section.D
Finally, we ensure this section of the string ends with a 'D'
The expression would match inputs such as ABBD
, ABCCCCD
, ABCBCCCD
and ACCCCCD
It most cases, it doesn't take very long for a regex engine to find a match:
$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCD")'
0.04s user 0.01s system 95% cpu 0.052 total
$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCX")'
1.79s user 0.02s system 99% cpu 1.812 total
The entire process of testing it against a 30 characters long string takes around ~52ms. But when given an invalid string, it takes nearly two seconds to complete the test, over ten times as long as it took to test a valid string. The dramatic difference is due to the way regular expressions get evaluated.
Most Regex engines will work very similarly (with minor differences). The engine will match the first possible way to accept the current character and proceed to the next one. If it then fails to match the next one, it will backtrack and see if there was another way to digest the previous character. If it goes too far down the rabbit hole only to find out the string doesn’t match in the end, and if many characters have multiple valid regex paths, the number of backtracking steps can become very large, resulting in what is known as catastrophic backtracking.
Let's look at how our expression runs into this problem, using a shorter string: "ACCCX". While it seems fairly straightforward, there are still four different ways that the engine could match those three C's:
- CCC
- CC+C
- C+CC
- C+C+C.
The engine has to try each of those combinations to see if any of them potentially match against the expression. When you combine that with the other steps the engine must take, we can use RegEx 101 debugger to see the engine has to take a total of 38 steps before it can determine the string doesn't match.
From there, the number of steps the engine must use to validate a string just continues to grow.
String | Number of C's | Number of steps |
---|---|---|
ACCCX | 3 | 38 |
ACCCCX | 4 | 71 |
ACCCCCX | 5 | 136 |
ACCCCCCCCCCCCCCX | 14 | 65,553 |
By the time the string includes 14 C's, the engine has to take over 65,000 steps just to see if the string is valid. These extreme situations can cause them to work very slowly (exponentially related to input size, as shown above), allowing an attacker to exploit this and can cause the service to excessively consume CPU, resulting in a Denial of Service.
Remediation
Upgrade handlebars
to version 4.4.5 or higher.
References
high severity
- Vulnerable module: hapi
- Introduced through: hapi@16.1.1
Detailed paths
-
Introduced through: hapi-rethinkdb-dash@fs-opensource/hapi-rethinkdb-dash#bde4f9de489cd0cdc2a4ebfa78de82a44b15e1f9 › hapi@16.1.1
Overview
hapi is a HTTP Server framework.
Affected versions of this package are vulnerable to Denial of Service (DoS). The CORS request handler has a vulnerability which will cause the function to throw a system error if the header contains some invalid values. If no unhandled exception handler is available, the application will exist, allowing an attacker to shut down services.
Details
Denial of Service (DoS) describes a family of attacks, all aimed at making a system inaccessible to its original and legitimate users. There are many types of DoS attacks, ranging from trying to clog the network pipes to the system by generating a large volume of traffic from many machines (a Distributed Denial of Service - DDoS - attack) to sending crafted requests that cause a system to crash or take a disproportional amount of time to process.
The Regular expression Denial of Service (ReDoS) is a type of Denial of Service attack. Regular expressions are incredibly powerful, but they aren't very intuitive and can ultimately end up making it easy for attackers to take your site down.
Let’s take the following regular expression as an example:
regex = /A(B|C+)+D/
This regular expression accomplishes the following:
A
The string must start with the letter 'A'(B|C+)+
The string must then follow the letter A with either the letter 'B' or some number of occurrences of the letter 'C' (the+
matches one or more times). The+
at the end of this section states that we can look for one or more matches of this section.D
Finally, we ensure this section of the string ends with a 'D'
The expression would match inputs such as ABBD
, ABCCCCD
, ABCBCCCD
and ACCCCCD
It most cases, it doesn't take very long for a regex engine to find a match:
$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCD")'
0.04s user 0.01s system 95% cpu 0.052 total
$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCX")'
1.79s user 0.02s system 99% cpu 1.812 total
The entire process of testing it against a 30 characters long string takes around ~52ms. But when given an invalid string, it takes nearly two seconds to complete the test, over ten times as long as it took to test a valid string. The dramatic difference is due to the way regular expressions get evaluated.
Most Regex engines will work very similarly (with minor differences). The engine will match the first possible way to accept the current character and proceed to the next one. If it then fails to match the next one, it will backtrack and see if there was another way to digest the previous character. If it goes too far down the rabbit hole only to find out the string doesn’t match in the end, and if many characters have multiple valid regex paths, the number of backtracking steps can become very large, resulting in what is known as catastrophic backtracking.
Let's look at how our expression runs into this problem, using a shorter string: "ACCCX". While it seems fairly straightforward, there are still four different ways that the engine could match those three C's:
- CCC
- CC+C
- C+CC
- C+C+C.
The engine has to try each of those combinations to see if any of them potentially match against the expression. When you combine that with the other steps the engine must take, we can use RegEx 101 debugger to see the engine has to take a total of 38 steps before it can determine the string doesn't match.
From there, the number of steps the engine must use to validate a string just continues to grow.
String | Number of C's | Number of steps |
---|---|---|
ACCCX | 3 | 38 |
ACCCCX | 4 | 71 |
ACCCCCX | 5 | 136 |
ACCCCCCCCCCCCCCX | 14 | 65,553 |
By the time the string includes 14 C's, the engine has to take over 65,000 steps just to see if the string is valid. These extreme situations can cause them to work very slowly (exponentially related to input size, as shown above), allowing an attacker to exploit this and can cause the service to excessively consume CPU, resulting in a Denial of Service.
Remediation
There is no fixed version for hapi
.
References
high severity
- Vulnerable module: html-to-text
- Introduced through: html-to-text@3.2.0
Detailed paths
-
Introduced through: hapi-rethinkdb-dash@fs-opensource/hapi-rethinkdb-dash#bde4f9de489cd0cdc2a4ebfa78de82a44b15e1f9 › html-to-text@3.2.0Remediation: Upgrade to html-to-text@6.0.0.
Overview
html-to-text is an Advanced html to plain text converter
Affected versions of this package are vulnerable to Denial of Service (DoS). The application may crash when the parsed HTML is either very deep or has a big amount of DOM elements.
Details
Denial of Service (DoS) describes a family of attacks, all aimed at making a system inaccessible to its intended and legitimate users.
Unlike other vulnerabilities, DoS attacks usually do not aim at breaching security. Rather, they are focused on making websites and services unavailable to genuine users resulting in downtime.
One popular Denial of Service vulnerability is DDoS (a Distributed Denial of Service), an attack that attempts to clog network pipes to the system by generating a large volume of traffic from many machines.
When it comes to open source libraries, DoS vulnerabilities allow attackers to trigger such a crash or crippling of the service by using a flaw either in the application code or from the use of open source libraries.
Two common types of DoS vulnerabilities:
High CPU/Memory Consumption- An attacker sending crafted requests that could cause the system to take a disproportionate amount of time to process. For example, commons-fileupload:commons-fileupload.
Crash - An attacker sending crafted requests that could cause the system to crash. For Example, npm
ws
package
Remediation
Upgrade html-to-text
to version 6.0.0 or higher.
References
high severity
- Vulnerable module: moment
- Introduced through: good-console@6.4.1
Detailed paths
-
Introduced through: hapi-rethinkdb-dash@fs-opensource/hapi-rethinkdb-dash#bde4f9de489cd0cdc2a4ebfa78de82a44b15e1f9 › good-console@6.4.1 › moment@2.19.4Remediation: Upgrade to good-console@8.0.0.
Overview
moment is a lightweight JavaScript date library for parsing, validating, manipulating, and formatting dates.
Affected versions of this package are vulnerable to Directory Traversal when a user provides a locale string which is directly used to switch moment locale.
Details
A Directory Traversal attack (also known as path traversal) aims to access files and directories that are stored outside the intended folder. By manipulating files with "dot-dot-slash (../)" sequences and its variations, or by using absolute file paths, it may be possible to access arbitrary files and directories stored on file system, including application source code, configuration, and other critical system files.
Directory Traversal vulnerabilities can be generally divided into two types:
- Information Disclosure: Allows the attacker to gain information about the folder structure or read the contents of sensitive files on the system.
st
is a module for serving static files on web pages, and contains a vulnerability of this type. In our example, we will serve files from the public
route.
If an attacker requests the following URL from our server, it will in turn leak the sensitive private key of the root user.
curl http://localhost:8080/public/%2e%2e/%2e%2e/%2e%2e/%2e%2e/%2e%2e/root/.ssh/id_rsa
Note %2e
is the URL encoded version of .
(dot).
- Writing arbitrary files: Allows the attacker to create or replace existing files. This type of vulnerability is also known as
Zip-Slip
.
One way to achieve this is by using a malicious zip
archive that holds path traversal filenames. When each filename in the zip archive gets concatenated to the target extraction folder, without validation, the final path ends up outside of the target folder. If an executable or a configuration file is overwritten with a file containing malicious code, the problem can turn into an arbitrary code execution issue quite easily.
The following is an example of a zip
archive with one benign file and one malicious file. Extracting the malicious file will result in traversing out of the target folder, ending up in /root/.ssh/
overwriting the authorized_keys
file:
2018-04-15 22:04:29 ..... 19 19 good.txt
2018-04-15 22:04:42 ..... 20 20 ../../../../../../root/.ssh/authorized_keys
Remediation
Upgrade moment
to version 2.29.2 or higher.
References
high severity
- Vulnerable module: moment
- Introduced through: good-console@6.4.1
Detailed paths
-
Introduced through: hapi-rethinkdb-dash@fs-opensource/hapi-rethinkdb-dash#bde4f9de489cd0cdc2a4ebfa78de82a44b15e1f9 › good-console@6.4.1 › moment@2.19.4Remediation: Upgrade to good-console@8.0.0.
Overview
moment is a lightweight JavaScript date library for parsing, validating, manipulating, and formatting dates.
Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS) via the preprocessRFC2822()
function in from-string.js
, when processing a very long crafted string (over 10k characters).
PoC:
moment("(".repeat(500000))
Details
Denial of Service (DoS) describes a family of attacks, all aimed at making a system inaccessible to its original and legitimate users. There are many types of DoS attacks, ranging from trying to clog the network pipes to the system by generating a large volume of traffic from many machines (a Distributed Denial of Service - DDoS - attack) to sending crafted requests that cause a system to crash or take a disproportional amount of time to process.
The Regular expression Denial of Service (ReDoS) is a type of Denial of Service attack. Regular expressions are incredibly powerful, but they aren't very intuitive and can ultimately end up making it easy for attackers to take your site down.
Let’s take the following regular expression as an example:
regex = /A(B|C+)+D/
This regular expression accomplishes the following:
A
The string must start with the letter 'A'(B|C+)+
The string must then follow the letter A with either the letter 'B' or some number of occurrences of the letter 'C' (the+
matches one or more times). The+
at the end of this section states that we can look for one or more matches of this section.D
Finally, we ensure this section of the string ends with a 'D'
The expression would match inputs such as ABBD
, ABCCCCD
, ABCBCCCD
and ACCCCCD
It most cases, it doesn't take very long for a regex engine to find a match:
$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCD")'
0.04s user 0.01s system 95% cpu 0.052 total
$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCX")'
1.79s user 0.02s system 99% cpu 1.812 total
The entire process of testing it against a 30 characters long string takes around ~52ms. But when given an invalid string, it takes nearly two seconds to complete the test, over ten times as long as it took to test a valid string. The dramatic difference is due to the way regular expressions get evaluated.
Most Regex engines will work very similarly (with minor differences). The engine will match the first possible way to accept the current character and proceed to the next one. If it then fails to match the next one, it will backtrack and see if there was another way to digest the previous character. If it goes too far down the rabbit hole only to find out the string doesn’t match in the end, and if many characters have multiple valid regex paths, the number of backtracking steps can become very large, resulting in what is known as catastrophic backtracking.
Let's look at how our expression runs into this problem, using a shorter string: "ACCCX". While it seems fairly straightforward, there are still four different ways that the engine could match those three C's:
- CCC
- CC+C
- C+CC
- C+C+C.
The engine has to try each of those combinations to see if any of them potentially match against the expression. When you combine that with the other steps the engine must take, we can use RegEx 101 debugger to see the engine has to take a total of 38 steps before it can determine the string doesn't match.
From there, the number of steps the engine must use to validate a string just continues to grow.
String | Number of C's | Number of steps |
---|---|---|
ACCCX | 3 | 38 |
ACCCCX | 4 | 71 |
ACCCCCX | 5 | 136 |
ACCCCCCCCCCCCCCX | 14 | 65,553 |
By the time the string includes 14 C's, the engine has to take over 65,000 steps just to see if the string is valid. These extreme situations can cause them to work very slowly (exponentially related to input size, as shown above), allowing an attacker to exploit this and can cause the service to excessively consume CPU, resulting in a Denial of Service.
Remediation
Upgrade moment
to version 2.29.4 or higher.
References
high severity
- Vulnerable module: subtext
- Introduced through: hapi@16.1.1
Detailed paths
-
Introduced through: hapi-rethinkdb-dash@fs-opensource/hapi-rethinkdb-dash#bde4f9de489cd0cdc2a4ebfa78de82a44b15e1f9 › hapi@16.1.1 › subtext@4.4.1
Overview
subtext is a HTTP payload parsing library. Deprecated. Note: This package is deprecated and is now maintained as @hapi/subtext
Affected versions of this package are vulnerable to Denial of Service (DoS).
The package fails to enforce the maxBytes
configuration for payloads with chunked encoding that are written to the file system. This allows attackers to send requests with arbitrary payload sizes, which may exhaust system resources.
Details
Denial of Service (DoS) describes a family of attacks, all aimed at making a system inaccessible to its intended and legitimate users.
Unlike other vulnerabilities, DoS attacks usually do not aim at breaching security. Rather, they are focused on making websites and services unavailable to genuine users resulting in downtime.
One popular Denial of Service vulnerability is DDoS (a Distributed Denial of Service), an attack that attempts to clog network pipes to the system by generating a large volume of traffic from many machines.
When it comes to open source libraries, DoS vulnerabilities allow attackers to trigger such a crash or crippling of the service by using a flaw either in the application code or from the use of open source libraries.
Two common types of DoS vulnerabilities:
High CPU/Memory Consumption- An attacker sending crafted requests that could cause the system to take a disproportionate amount of time to process. For example, commons-fileupload:commons-fileupload.
Crash - An attacker sending crafted requests that could cause the system to crash. For Example, npm
ws
package
Remediation
There is no fixed version for subtext
.
References
high severity
- Vulnerable module: subtext
- Introduced through: hapi@16.1.1
Detailed paths
-
Introduced through: hapi-rethinkdb-dash@fs-opensource/hapi-rethinkdb-dash#bde4f9de489cd0cdc2a4ebfa78de82a44b15e1f9 › hapi@16.1.1 › subtext@4.4.1
Overview
subtext is a HTTP payload parsing library. Deprecated. Note: This package is deprecated and is now maintained as @hapi/subtext
Affected versions of this package are vulnerable to Denial of Service (DoS). The Content-Encoding HTTP header parser has a vulnerability which will cause the function to throw a system error if the header contains some invalid values. Because hapi rethrows system errors (as opposed to catching expected application errors), the error is thrown all the way up the stack. If no unhandled exception handler is available, the application will exist, allowing an attacker to shut down services.
Details
Denial of Service (DoS) describes a family of attacks, all aimed at making a system inaccessible to its original and legitimate users. There are many types of DoS attacks, ranging from trying to clog the network pipes to the system by generating a large volume of traffic from many machines (a Distributed Denial of Service - DDoS - attack) to sending crafted requests that cause a system to crash or take a disproportional amount of time to process.
The Regular expression Denial of Service (ReDoS) is a type of Denial of Service attack. Regular expressions are incredibly powerful, but they aren't very intuitive and can ultimately end up making it easy for attackers to take your site down.
Let’s take the following regular expression as an example:
regex = /A(B|C+)+D/
This regular expression accomplishes the following:
A
The string must start with the letter 'A'(B|C+)+
The string must then follow the letter A with either the letter 'B' or some number of occurrences of the letter 'C' (the+
matches one or more times). The+
at the end of this section states that we can look for one or more matches of this section.D
Finally, we ensure this section of the string ends with a 'D'
The expression would match inputs such as ABBD
, ABCCCCD
, ABCBCCCD
and ACCCCCD
It most cases, it doesn't take very long for a regex engine to find a match:
$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCD")'
0.04s user 0.01s system 95% cpu 0.052 total
$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCX")'
1.79s user 0.02s system 99% cpu 1.812 total
The entire process of testing it against a 30 characters long string takes around ~52ms. But when given an invalid string, it takes nearly two seconds to complete the test, over ten times as long as it took to test a valid string. The dramatic difference is due to the way regular expressions get evaluated.
Most Regex engines will work very similarly (with minor differences). The engine will match the first possible way to accept the current character and proceed to the next one. If it then fails to match the next one, it will backtrack and see if there was another way to digest the previous character. If it goes too far down the rabbit hole only to find out the string doesn’t match in the end, and if many characters have multiple valid regex paths, the number of backtracking steps can become very large, resulting in what is known as catastrophic backtracking.
Let's look at how our expression runs into this problem, using a shorter string: "ACCCX". While it seems fairly straightforward, there are still four different ways that the engine could match those three C's:
- CCC
- CC+C
- C+CC
- C+C+C.
The engine has to try each of those combinations to see if any of them potentially match against the expression. When you combine that with the other steps the engine must take, we can use RegEx 101 debugger to see the engine has to take a total of 38 steps before it can determine the string doesn't match.
From there, the number of steps the engine must use to validate a string just continues to grow.
String | Number of C's | Number of steps |
---|---|---|
ACCCX | 3 | 38 |
ACCCCX | 4 | 71 |
ACCCCCX | 5 | 136 |
ACCCCCCCCCCCCCCX | 14 | 65,553 |
By the time the string includes 14 C's, the engine has to take over 65,000 steps just to see if the string is valid. These extreme situations can cause them to work very slowly (exponentially related to input size, as shown above), allowing an attacker to exploit this and can cause the service to excessively consume CPU, resulting in a Denial of Service.
Remediation
There is no fixed version for subtext
.
References
high severity
- Vulnerable module: handlebars
- Introduced through: handlebars@4.0.14
Detailed paths
-
Introduced through: hapi-rethinkdb-dash@fs-opensource/hapi-rethinkdb-dash#bde4f9de489cd0cdc2a4ebfa78de82a44b15e1f9 › handlebars@4.0.14Remediation: Upgrade to handlebars@4.3.0.
Overview
handlebars is a extension to the Mustache templating language.
Affected versions of this package are vulnerable to Prototype Pollution.
Templates may alter an Object's __proto__
and __defineGetter__
properties, which may allow an attacker to execute arbitrary code on the server through crafted payloads.
Details
Prototype Pollution is a vulnerability affecting JavaScript. Prototype Pollution refers to the ability to inject properties into existing JavaScript language construct prototypes, such as objects. JavaScript allows all Object attributes to be altered, including their magical attributes such as _proto_
, constructor
and prototype
. An attacker manipulates these attributes to overwrite, or pollute, a JavaScript application object prototype of the base object by injecting other values. Properties on the Object.prototype
are then inherited by all the JavaScript objects through the prototype chain. When that happens, this leads to either denial of service by triggering JavaScript exceptions, or it tampers with the application source code to force the code path that the attacker injects, thereby leading to remote code execution.
There are two main ways in which the pollution of prototypes occurs:
Unsafe
Object
recursive mergeProperty definition by path
Unsafe Object recursive merge
The logic of a vulnerable recursive merge function follows the following high-level model:
merge (target, source)
foreach property of source
if property exists and is an object on both the target and the source
merge(target[property], source[property])
else
target[property] = source[property]
When the source object contains a property named _proto_
defined with Object.defineProperty()
, the condition that checks if the property exists and is an object on both the target and the source passes and the merge recurses with the target, being the prototype of Object
and the source of Object
as defined by the attacker. Properties are then copied on the Object
prototype.
Clone operations are a special sub-class of unsafe recursive merges, which occur when a recursive merge is conducted on an empty object: merge({},source)
.
lodash
and Hoek
are examples of libraries susceptible to recursive merge attacks.
Property definition by path
There are a few JavaScript libraries that use an API to define property values on an object based on a given path. The function that is generally affected contains this signature: theFunction(object, path, value)
If the attacker can control the value of “path”, they can set this value to _proto_.myValue
. myValue
is then assigned to the prototype of the class of the object.
Types of attacks
There are a few methods by which Prototype Pollution can be manipulated:
Type | Origin | Short description |
---|---|---|
Denial of service (DoS) | Client | This is the most likely attack. DoS occurs when Object holds generic functions that are implicitly called for various operations (for example, toString and valueOf ). The attacker pollutes Object.prototype.someattr and alters its state to an unexpected value such as Int or Object . In this case, the code fails and is likely to cause a denial of service. For example: if an attacker pollutes Object.prototype.toString by defining it as an integer, if the codebase at any point was reliant on someobject.toString() it would fail. |
Remote Code Execution | Client | Remote code execution is generally only possible in cases where the codebase evaluates a specific attribute of an object, and then executes that evaluation. For example: eval(someobject.someattr) . In this case, if the attacker pollutes Object.prototype.someattr they are likely to be able to leverage this in order to execute code. |
Property Injection | Client | The attacker pollutes properties that the codebase relies on for their informative value, including security properties such as cookies or tokens. For example: if a codebase checks privileges for someuser.isAdmin , then when the attacker pollutes Object.prototype.isAdmin and sets it to equal true , they can then achieve admin privileges. |
Affected environments
The following environments are susceptible to a Prototype Pollution attack:
Application server
Web server
How to prevent
Freeze the prototype— use
Object.freeze (Object.prototype)
.Require schema validation of JSON input.
Avoid using unsafe recursive merge functions.
Consider using objects without prototypes (for example,
Object.create(null)
), breaking the prototype chain and preventing pollution.As a best practice use
Map
instead ofObject
.
For more information on this vulnerability type:
Arteau, Oliver. “JavaScript prototype pollution attack in NodeJS application.” GitHub, 26 May 2018
Remediation
Upgrade handlebars
to version 4.3.0, 3.0.8 or higher.
References
high severity
- Vulnerable module: subtext
- Introduced through: hapi@16.1.1
Detailed paths
-
Introduced through: hapi-rethinkdb-dash@fs-opensource/hapi-rethinkdb-dash#bde4f9de489cd0cdc2a4ebfa78de82a44b15e1f9 › hapi@16.1.1 › subtext@4.4.1
Overview
subtext is a HTTP payload parsing library. Deprecated. Note: This package is deprecated and is now maintained as @hapi/subtext
Affected versions of this package are vulnerable to Prototype Pollution. A multipart payload can be constructed in a way that one of the parts’ content can be set as the entire payload object’s prototype. If this prototype contains data, it may bypass other validation rules which enforce access and privacy. If this prototype evaluates to null, it can cause unhandled exceptions when the request payload is accessed.
Details
Denial of Service (DoS) describes a family of attacks, all aimed at making a system inaccessible to its original and legitimate users. There are many types of DoS attacks, ranging from trying to clog the network pipes to the system by generating a large volume of traffic from many machines (a Distributed Denial of Service - DDoS - attack) to sending crafted requests that cause a system to crash or take a disproportional amount of time to process.
The Regular expression Denial of Service (ReDoS) is a type of Denial of Service attack. Regular expressions are incredibly powerful, but they aren't very intuitive and can ultimately end up making it easy for attackers to take your site down.
Let’s take the following regular expression as an example:
regex = /A(B|C+)+D/
This regular expression accomplishes the following:
A
The string must start with the letter 'A'(B|C+)+
The string must then follow the letter A with either the letter 'B' or some number of occurrences of the letter 'C' (the+
matches one or more times). The+
at the end of this section states that we can look for one or more matches of this section.D
Finally, we ensure this section of the string ends with a 'D'
The expression would match inputs such as ABBD
, ABCCCCD
, ABCBCCCD
and ACCCCCD
It most cases, it doesn't take very long for a regex engine to find a match:
$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCD")'
0.04s user 0.01s system 95% cpu 0.052 total
$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCX")'
1.79s user 0.02s system 99% cpu 1.812 total
The entire process of testing it against a 30 characters long string takes around ~52ms. But when given an invalid string, it takes nearly two seconds to complete the test, over ten times as long as it took to test a valid string. The dramatic difference is due to the way regular expressions get evaluated.
Most Regex engines will work very similarly (with minor differences). The engine will match the first possible way to accept the current character and proceed to the next one. If it then fails to match the next one, it will backtrack and see if there was another way to digest the previous character. If it goes too far down the rabbit hole only to find out the string doesn’t match in the end, and if many characters have multiple valid regex paths, the number of backtracking steps can become very large, resulting in what is known as catastrophic backtracking.
Let's look at how our expression runs into this problem, using a shorter string: "ACCCX". While it seems fairly straightforward, there are still four different ways that the engine could match those three C's:
- CCC
- CC+C
- C+CC
- C+C+C.
The engine has to try each of those combinations to see if any of them potentially match against the expression. When you combine that with the other steps the engine must take, we can use RegEx 101 debugger to see the engine has to take a total of 38 steps before it can determine the string doesn't match.
From there, the number of steps the engine must use to validate a string just continues to grow.
String | Number of C's | Number of steps |
---|---|---|
ACCCX | 3 | 38 |
ACCCCX | 4 | 71 |
ACCCCCX | 5 | 136 |
ACCCCCCCCCCCCCCX | 14 | 65,553 |
By the time the string includes 14 C's, the engine has to take over 65,000 steps just to see if the string is valid. These extreme situations can cause them to work very slowly (exponentially related to input size, as shown above), allowing an attacker to exploit this and can cause the service to excessively consume CPU, resulting in a Denial of Service.
Remediation
There is no fixed version for subtext
.
References
high severity
- Vulnerable module: handlebars
- Introduced through: handlebars@4.0.14
Detailed paths
-
Introduced through: hapi-rethinkdb-dash@fs-opensource/hapi-rethinkdb-dash#bde4f9de489cd0cdc2a4ebfa78de82a44b15e1f9 › handlebars@4.0.14Remediation: Upgrade to handlebars@4.7.7.
Overview
handlebars is an extension to the Mustache templating language.
Affected versions of this package are vulnerable to Remote Code Execution (RCE) when selecting certain compiling options to compile templates coming from an untrusted source.
POC
<script src="https://cdn.jsdelivr.net/npm/handlebars@latest/dist/handlebars.js"></script>
<script>
// compile the template
var s = `
{{#with (__lookupGetter__ "__proto__")}}
{{#with (./constructor.getOwnPropertyDescriptor . "valueOf")}}
{{#with ../constructor.prototype}}
{{../../constructor.defineProperty . "hasOwnProperty" ..}}
{{/with}}
{{/with}}
{{/with}}
{{#with "constructor"}}
{{#with split}}
{{pop (push "alert('Vulnerable Handlebars JS when compiling in strict mode');")}}
{{#with .}}
{{#with (concat (lookup join (slice 0 1)))}}
{{#each (slice 2 3)}}
{{#with (apply 0 ../..)}}
{{.}}
{{/with}}
{{/each}}
{{/with}}
{{/with}}
{{/with}}
{{/with}}
`;
var template = Handlebars.compile(s, {
strict: true
});
// execute the compiled template and print the output to the console console.log(template({}));
</script>
Remediation
Upgrade handlebars
to version 4.7.7 or higher.
References
medium severity
- Vulnerable module: handlebars
- Introduced through: handlebars@4.0.14
Detailed paths
-
Introduced through: hapi-rethinkdb-dash@fs-opensource/hapi-rethinkdb-dash#bde4f9de489cd0cdc2a4ebfa78de82a44b15e1f9 › handlebars@4.0.14Remediation: Upgrade to handlebars@4.6.0.
Overview
handlebars is an extension to the Mustache templating language.
Affected versions of this package are vulnerable to Prototype Pollution. Prototype access to the template engine allows for potential code execution.
Details
Denial of Service (DoS) describes a family of attacks, all aimed at making a system inaccessible to its original and legitimate users. There are many types of DoS attacks, ranging from trying to clog the network pipes to the system by generating a large volume of traffic from many machines (a Distributed Denial of Service - DDoS - attack) to sending crafted requests that cause a system to crash or take a disproportional amount of time to process.
The Regular expression Denial of Service (ReDoS) is a type of Denial of Service attack. Regular expressions are incredibly powerful, but they aren't very intuitive and can ultimately end up making it easy for attackers to take your site down.
Let’s take the following regular expression as an example:
regex = /A(B|C+)+D/
This regular expression accomplishes the following:
A
The string must start with the letter 'A'(B|C+)+
The string must then follow the letter A with either the letter 'B' or some number of occurrences of the letter 'C' (the+
matches one or more times). The+
at the end of this section states that we can look for one or more matches of this section.D
Finally, we ensure this section of the string ends with a 'D'
The expression would match inputs such as ABBD
, ABCCCCD
, ABCBCCCD
and ACCCCCD
It most cases, it doesn't take very long for a regex engine to find a match:
$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCD")'
0.04s user 0.01s system 95% cpu 0.052 total
$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCX")'
1.79s user 0.02s system 99% cpu 1.812 total
The entire process of testing it against a 30 characters long string takes around ~52ms. But when given an invalid string, it takes nearly two seconds to complete the test, over ten times as long as it took to test a valid string. The dramatic difference is due to the way regular expressions get evaluated.
Most Regex engines will work very similarly (with minor differences). The engine will match the first possible way to accept the current character and proceed to the next one. If it then fails to match the next one, it will backtrack and see if there was another way to digest the previous character. If it goes too far down the rabbit hole only to find out the string doesn’t match in the end, and if many characters have multiple valid regex paths, the number of backtracking steps can become very large, resulting in what is known as catastrophic backtracking.
Let's look at how our expression runs into this problem, using a shorter string: "ACCCX". While it seems fairly straightforward, there are still four different ways that the engine could match those three C's:
- CCC
- CC+C
- C+CC
- C+C+C.
The engine has to try each of those combinations to see if any of them potentially match against the expression. When you combine that with the other steps the engine must take, we can use RegEx 101 debugger to see the engine has to take a total of 38 steps before it can determine the string doesn't match.
From there, the number of steps the engine must use to validate a string just continues to grow.
String | Number of C's | Number of steps |
---|---|---|
ACCCX | 3 | 38 |
ACCCCX | 4 | 71 |
ACCCCCX | 5 | 136 |
ACCCCCCCCCCCCCCX | 14 | 65,553 |
By the time the string includes 14 C's, the engine has to take over 65,000 steps just to see if the string is valid. These extreme situations can cause them to work very slowly (exponentially related to input size, as shown above), allowing an attacker to exploit this and can cause the service to excessively consume CPU, resulting in a Denial of Service.
Remediation
Upgrade handlebars
to version 4.6.0 or higher.
References
medium severity
- Vulnerable module: request
- Introduced through: bcrypt@1.0.3
Detailed paths
-
Introduced through: hapi-rethinkdb-dash@fs-opensource/hapi-rethinkdb-dash#bde4f9de489cd0cdc2a4ebfa78de82a44b15e1f9 › bcrypt@1.0.3 › node-pre-gyp@0.6.36 › request@2.88.2
Overview
request is a simplified http request client.
Affected versions of this package are vulnerable to Server-side Request Forgery (SSRF) due to insufficient checks in the lib/redirect.js
file by allowing insecure redirects in the default configuration, via an attacker-controller server that does a cross-protocol redirect (HTTP to HTTPS, or HTTPS to HTTP).
NOTE: request
package has been deprecated, so a fix is not expected. See https://github.com/request/request/issues/3142.
Remediation
A fix was pushed into the master
branch but not yet published.
References
medium severity
- Vulnerable module: tar
- Introduced through: bcrypt@1.0.3
Detailed paths
-
Introduced through: hapi-rethinkdb-dash@fs-opensource/hapi-rethinkdb-dash#bde4f9de489cd0cdc2a4ebfa78de82a44b15e1f9 › bcrypt@1.0.3 › node-pre-gyp@0.6.36 › tar@2.2.2
-
Introduced through: hapi-rethinkdb-dash@fs-opensource/hapi-rethinkdb-dash#bde4f9de489cd0cdc2a4ebfa78de82a44b15e1f9 › bcrypt@1.0.3 › node-pre-gyp@0.6.36 › tar-pack@3.4.1 › tar@2.2.2
Overview
tar is a full-featured Tar for Node.js.
Affected versions of this package are vulnerable to Uncontrolled Resource Consumption ('Resource Exhaustion') due to the lack of folders count validation during the folder creation process. An attacker who generates a large number of sub-folders can consume memory on the system running the software and even crash the client within few seconds of running it using a path with too many sub-folders inside.
Remediation
Upgrade tar
to version 6.2.1 or higher.
References
medium severity
- Vulnerable module: tough-cookie
- Introduced through: bcrypt@1.0.3
Detailed paths
-
Introduced through: hapi-rethinkdb-dash@fs-opensource/hapi-rethinkdb-dash#bde4f9de489cd0cdc2a4ebfa78de82a44b15e1f9 › bcrypt@1.0.3 › node-pre-gyp@0.6.36 › request@2.88.2 › tough-cookie@2.5.0
Overview
tough-cookie is a RFC6265 Cookies and CookieJar module for Node.js.
Affected versions of this package are vulnerable to Prototype Pollution due to improper handling of Cookies when using CookieJar in rejectPublicSuffixes=false
mode. Due to an issue with the manner in which the objects are initialized, an attacker can expose or modify a limited amount of property information on those objects. There is no impact to availability.
PoC
// PoC.js
async function main(){
var tough = require("tough-cookie");
var cookiejar = new tough.CookieJar(undefined,{rejectPublicSuffixes:false});
// Exploit cookie
await cookiejar.setCookie(
"Slonser=polluted; Domain=__proto__; Path=/notauth",
"https://__proto__/admin"
);
// normal cookie
var cookie = await cookiejar.setCookie(
"Auth=Lol; Domain=google.com; Path=/notauth",
"https://google.com/"
);
//Exploit cookie
var a = {};
console.log(a["/notauth"]["Slonser"])
}
main();
Details
Prototype Pollution is a vulnerability affecting JavaScript. Prototype Pollution refers to the ability to inject properties into existing JavaScript language construct prototypes, such as objects. JavaScript allows all Object attributes to be altered, including their magical attributes such as __proto__
, constructor
and prototype
. An attacker manipulates these attributes to overwrite, or pollute, a JavaScript application object prototype of the base object by injecting other values. Properties on the Object.prototype
are then inherited by all the JavaScript objects through the prototype chain. When that happens, this leads to either denial of service by triggering JavaScript exceptions, or it tampers with the application source code to force the code path that the attacker injects, thereby leading to remote code execution.
There are two main ways in which the pollution of prototypes occurs:
Unsafe
Object
recursive mergeProperty definition by path
Unsafe Object recursive merge
The logic of a vulnerable recursive merge function follows the following high-level model:
merge (target, source)
foreach property of source
if property exists and is an object on both the target and the source
merge(target[property], source[property])
else
target[property] = source[property]
When the source object contains a property named __proto__
defined with Object.defineProperty()
, the condition that checks if the property exists and is an object on both the target and the source passes and the merge recurses with the target, being the prototype of Object
and the source of Object
as defined by the attacker. Properties are then copied on the Object
prototype.
Clone operations are a special sub-class of unsafe recursive merges, which occur when a recursive merge is conducted on an empty object: merge({},source)
.
lodash
and Hoek
are examples of libraries susceptible to recursive merge attacks.
Property definition by path
There are a few JavaScript libraries that use an API to define property values on an object based on a given path. The function that is generally affected contains this signature: theFunction(object, path, value)
If the attacker can control the value of “path”, they can set this value to __proto__.myValue
. myValue
is then assigned to the prototype of the class of the object.
Types of attacks
There are a few methods by which Prototype Pollution can be manipulated:
Type | Origin | Short description |
---|---|---|
Denial of service (DoS) | Client | This is the most likely attack. DoS occurs when Object holds generic functions that are implicitly called for various operations (for example, toString and valueOf ). The attacker pollutes Object.prototype.someattr and alters its state to an unexpected value such as Int or Object . In this case, the code fails and is likely to cause a denial of service. For example: if an attacker pollutes Object.prototype.toString by defining it as an integer, if the codebase at any point was reliant on someobject.toString() it would fail. |
Remote Code Execution | Client | Remote code execution is generally only possible in cases where the codebase evaluates a specific attribute of an object, and then executes that evaluation. For example: eval(someobject.someattr) . In this case, if the attacker pollutes Object.prototype.someattr they are likely to be able to leverage this in order to execute code. |
Property Injection | Client | The attacker pollutes properties that the codebase relies on for their informative value, including security properties such as cookies or tokens. For example: if a codebase checks privileges for someuser.isAdmin , then when the attacker pollutes Object.prototype.isAdmin and sets it to equal true , they can then achieve admin privileges. |
Affected environments
The following environments are susceptible to a Prototype Pollution attack:
Application server
Web server
Web browser
How to prevent
Freeze the prototype— use
Object.freeze (Object.prototype)
.Require schema validation of JSON input.
Avoid using unsafe recursive merge functions.
Consider using objects without prototypes (for example,
Object.create(null)
), breaking the prototype chain and preventing pollution.As a best practice use
Map
instead ofObject
.
For more information on this vulnerability type:
Arteau, Oliver. “JavaScript prototype pollution attack in NodeJS application.” GitHub, 26 May 2018
Remediation
Upgrade tough-cookie
to version 4.1.3 or higher.
References
medium severity
- Vulnerable module: hoek
- Introduced through: hapi-auth-cookie@6.1.1
Detailed paths
-
Introduced through: hapi-rethinkdb-dash@fs-opensource/hapi-rethinkdb-dash#bde4f9de489cd0cdc2a4ebfa78de82a44b15e1f9 › hapi-auth-cookie@6.1.1 › hoek@3.0.4Remediation: Upgrade to hapi-auth-cookie@7.0.0.
-
Introduced through: hapi-rethinkdb-dash@fs-opensource/hapi-rethinkdb-dash#bde4f9de489cd0cdc2a4ebfa78de82a44b15e1f9 › hapi-auth-cookie@6.1.1 › joi@7.3.0 › hoek@3.0.4Remediation: Upgrade to hapi-auth-cookie@7.0.0.
Overview
hoek is an Utility methods for the hapi ecosystem.
Affected versions of this package are vulnerable to Prototype Pollution. The utilities function allow modification of the Object
prototype. If an attacker can control part of the structure passed to this function, they could add or modify an existing property.
PoC by Olivier Arteau (HoLyVieR)
var Hoek = require('hoek');
var malicious_payload = '{"__proto__":{"oops":"It works !"}}';
var a = {};
console.log("Before : " + a.oops);
Hoek.merge({}, JSON.parse(malicious_payload));
console.log("After : " + a.oops);
Details
Denial of Service (DoS) describes a family of attacks, all aimed at making a system inaccessible to its original and legitimate users. There are many types of DoS attacks, ranging from trying to clog the network pipes to the system by generating a large volume of traffic from many machines (a Distributed Denial of Service - DDoS - attack) to sending crafted requests that cause a system to crash or take a disproportional amount of time to process.
The Regular expression Denial of Service (ReDoS) is a type of Denial of Service attack. Regular expressions are incredibly powerful, but they aren't very intuitive and can ultimately end up making it easy for attackers to take your site down.
Let’s take the following regular expression as an example:
regex = /A(B|C+)+D/
This regular expression accomplishes the following:
A
The string must start with the letter 'A'(B|C+)+
The string must then follow the letter A with either the letter 'B' or some number of occurrences of the letter 'C' (the+
matches one or more times). The+
at the end of this section states that we can look for one or more matches of this section.D
Finally, we ensure this section of the string ends with a 'D'
The expression would match inputs such as ABBD
, ABCCCCD
, ABCBCCCD
and ACCCCCD
It most cases, it doesn't take very long for a regex engine to find a match:
$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCD")'
0.04s user 0.01s system 95% cpu 0.052 total
$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCX")'
1.79s user 0.02s system 99% cpu 1.812 total
The entire process of testing it against a 30 characters long string takes around ~52ms. But when given an invalid string, it takes nearly two seconds to complete the test, over ten times as long as it took to test a valid string. The dramatic difference is due to the way regular expressions get evaluated.
Most Regex engines will work very similarly (with minor differences). The engine will match the first possible way to accept the current character and proceed to the next one. If it then fails to match the next one, it will backtrack and see if there was another way to digest the previous character. If it goes too far down the rabbit hole only to find out the string doesn’t match in the end, and if many characters have multiple valid regex paths, the number of backtracking steps can become very large, resulting in what is known as catastrophic backtracking.
Let's look at how our expression runs into this problem, using a shorter string: "ACCCX". While it seems fairly straightforward, there are still four different ways that the engine could match those three C's:
- CCC
- CC+C
- C+CC
- C+C+C.
The engine has to try each of those combinations to see if any of them potentially match against the expression. When you combine that with the other steps the engine must take, we can use RegEx 101 debugger to see the engine has to take a total of 38 steps before it can determine the string doesn't match.
From there, the number of steps the engine must use to validate a string just continues to grow.
String | Number of C's | Number of steps |
---|---|---|
ACCCX | 3 | 38 |
ACCCCX | 4 | 71 |
ACCCCCX | 5 | 136 |
ACCCCCCCCCCCCCCX | 14 | 65,553 |
By the time the string includes 14 C's, the engine has to take over 65,000 steps just to see if the string is valid. These extreme situations can cause them to work very slowly (exponentially related to input size, as shown above), allowing an attacker to exploit this and can cause the service to excessively consume CPU, resulting in a Denial of Service.
Remediation
Upgrade hoek
to version 4.2.1, 5.0.3 or higher.
References
medium severity
- Vulnerable module: nodemailer
- Introduced through: nodemailer@3.1.5
Detailed paths
-
Introduced through: hapi-rethinkdb-dash@fs-opensource/hapi-rethinkdb-dash#bde4f9de489cd0cdc2a4ebfa78de82a44b15e1f9 › nodemailer@3.1.5Remediation: Upgrade to nodemailer@6.6.1.
Overview
nodemailer is an Easy as cake e-mail sending from your Node.js applications
Affected versions of this package are vulnerable to HTTP Header Injection if unsanitized user input that may contain newlines and carriage returns is passed into an address object.
PoC:
const userEmail = 'foo@bar.comrnSubject: foobar'; // imagine this comes from e.g. HTTP request params or is otherwise user-controllable
await transporter.sendMail({
from: '...',
to: '...',
replyTo: {
name: 'Customer',
address: userEmail,
},
subject: 'My Subject',
text: message,
});
Remediation
Upgrade nodemailer
to version 6.6.1 or higher.
References
medium severity
- Vulnerable module: inflight
- Introduced through: bcrypt@1.0.3
Detailed paths
-
Introduced through: hapi-rethinkdb-dash@fs-opensource/hapi-rethinkdb-dash#bde4f9de489cd0cdc2a4ebfa78de82a44b15e1f9 › bcrypt@1.0.3 › node-pre-gyp@0.6.36 › rimraf@2.7.1 › glob@7.2.3 › inflight@1.0.6
-
Introduced through: hapi-rethinkdb-dash@fs-opensource/hapi-rethinkdb-dash#bde4f9de489cd0cdc2a4ebfa78de82a44b15e1f9 › bcrypt@1.0.3 › node-pre-gyp@0.6.36 › tar-pack@3.4.1 › rimraf@2.7.1 › glob@7.2.3 › inflight@1.0.6
-
Introduced through: hapi-rethinkdb-dash@fs-opensource/hapi-rethinkdb-dash#bde4f9de489cd0cdc2a4ebfa78de82a44b15e1f9 › bcrypt@1.0.3 › node-pre-gyp@0.6.36 › tar@2.2.2 › fstream@1.0.12 › rimraf@2.7.1 › glob@7.2.3 › inflight@1.0.6
-
Introduced through: hapi-rethinkdb-dash@fs-opensource/hapi-rethinkdb-dash#bde4f9de489cd0cdc2a4ebfa78de82a44b15e1f9 › bcrypt@1.0.3 › node-pre-gyp@0.6.36 › tar-pack@3.4.1 › fstream@1.0.12 › rimraf@2.7.1 › glob@7.2.3 › inflight@1.0.6
-
Introduced through: hapi-rethinkdb-dash@fs-opensource/hapi-rethinkdb-dash#bde4f9de489cd0cdc2a4ebfa78de82a44b15e1f9 › bcrypt@1.0.3 › node-pre-gyp@0.6.36 › tar-pack@3.4.1 › tar@2.2.2 › fstream@1.0.12 › rimraf@2.7.1 › glob@7.2.3 › inflight@1.0.6
-
Introduced through: hapi-rethinkdb-dash@fs-opensource/hapi-rethinkdb-dash#bde4f9de489cd0cdc2a4ebfa78de82a44b15e1f9 › bcrypt@1.0.3 › node-pre-gyp@0.6.36 › tar-pack@3.4.1 › fstream-ignore@1.0.5 › fstream@1.0.12 › rimraf@2.7.1 › glob@7.2.3 › inflight@1.0.6
Overview
Affected versions of this package are vulnerable to Missing Release of Resource after Effective Lifetime via the makeres
function due to improperly deleting keys from the reqs
object after execution of callbacks. This behavior causes the keys to remain in the reqs
object, which leads to resource exhaustion.
Exploiting this vulnerability results in crashing the node
process or in the application crash.
Note: This library is not maintained, and currently, there is no fix for this issue. To overcome this vulnerability, several dependent packages have eliminated the use of this library.
To trigger the memory leak, an attacker would need to have the ability to execute or influence the asynchronous operations that use the inflight module within the application. This typically requires access to the internal workings of the server or application, which is not commonly exposed to remote users. Therefore, “Attack vector” is marked as “Local”.
PoC
const inflight = require('inflight');
function testInflight() {
let i = 0;
function scheduleNext() {
let key = `key-${i++}`;
const callback = () => {
};
for (let j = 0; j < 1000000; j++) {
inflight(key, callback);
}
setImmediate(scheduleNext);
}
if (i % 100 === 0) {
console.log(process.memoryUsage());
}
scheduleNext();
}
testInflight();
Remediation
There is no fixed version for inflight
.
References
medium severity
- Vulnerable module: bcrypt
- Introduced through: bcrypt@1.0.3
Detailed paths
-
Introduced through: hapi-rethinkdb-dash@fs-opensource/hapi-rethinkdb-dash#bde4f9de489cd0cdc2a4ebfa78de82a44b15e1f9 › bcrypt@1.0.3Remediation: Upgrade to bcrypt@5.0.0.
Overview
bcrypt is an A library to help you hash passwords.
Affected versions of this package are vulnerable to Cryptographic Issues. When hashing a password containing an ASCII NUL character, that character acts as the string terminator. Any following characters are ignored.
Remediation
Upgrade bcrypt
to version 5.0.0 or higher.
References
medium severity
- Vulnerable module: handlebars
- Introduced through: handlebars@4.0.14
Detailed paths
-
Introduced through: hapi-rethinkdb-dash@fs-opensource/hapi-rethinkdb-dash#bde4f9de489cd0cdc2a4ebfa78de82a44b15e1f9 › handlebars@4.0.14Remediation: Upgrade to handlebars@4.7.7.
Overview
handlebars is an extension to the Mustache templating language.
Affected versions of this package are vulnerable to Prototype Pollution when selecting certain compiling options to compile templates coming from an untrusted source.
POC
<script src="https://cdn.jsdelivr.net/npm/handlebars@latest/dist/handlebars.js"></script>
<script>
// compile the template
var s2 = `{{'a/.") || alert("Vulnerable Handlebars JS when compiling in compat mode'}}`;
var template = Handlebars.compile(s2, {
compat: true
});
// execute the compiled template and print the output to the console console.log(template({}));
</script>
Details
Prototype Pollution is a vulnerability affecting JavaScript. Prototype Pollution refers to the ability to inject properties into existing JavaScript language construct prototypes, such as objects. JavaScript allows all Object attributes to be altered, including their magical attributes such as __proto__
, constructor
and prototype
. An attacker manipulates these attributes to overwrite, or pollute, a JavaScript application object prototype of the base object by injecting other values. Properties on the Object.prototype
are then inherited by all the JavaScript objects through the prototype chain. When that happens, this leads to either denial of service by triggering JavaScript exceptions, or it tampers with the application source code to force the code path that the attacker injects, thereby leading to remote code execution.
There are two main ways in which the pollution of prototypes occurs:
Unsafe
Object
recursive mergeProperty definition by path
Unsafe Object recursive merge
The logic of a vulnerable recursive merge function follows the following high-level model:
merge (target, source)
foreach property of source
if property exists and is an object on both the target and the source
merge(target[property], source[property])
else
target[property] = source[property]
When the source object contains a property named __proto__
defined with Object.defineProperty()
, the condition that checks if the property exists and is an object on both the target and the source passes and the merge recurses with the target, being the prototype of Object
and the source of Object
as defined by the attacker. Properties are then copied on the Object
prototype.
Clone operations are a special sub-class of unsafe recursive merges, which occur when a recursive merge is conducted on an empty object: merge({},source)
.
lodash
and Hoek
are examples of libraries susceptible to recursive merge attacks.
Property definition by path
There are a few JavaScript libraries that use an API to define property values on an object based on a given path. The function that is generally affected contains this signature: theFunction(object, path, value)
If the attacker can control the value of “path”, they can set this value to __proto__.myValue
. myValue
is then assigned to the prototype of the class of the object.
Types of attacks
There are a few methods by which Prototype Pollution can be manipulated:
Type | Origin | Short description |
---|---|---|
Denial of service (DoS) | Client | This is the most likely attack. DoS occurs when Object holds generic functions that are implicitly called for various operations (for example, toString and valueOf ). The attacker pollutes Object.prototype.someattr and alters its state to an unexpected value such as Int or Object . In this case, the code fails and is likely to cause a denial of service. For example: if an attacker pollutes Object.prototype.toString by defining it as an integer, if the codebase at any point was reliant on someobject.toString() it would fail. |
Remote Code Execution | Client | Remote code execution is generally only possible in cases where the codebase evaluates a specific attribute of an object, and then executes that evaluation. For example: eval(someobject.someattr) . In this case, if the attacker pollutes Object.prototype.someattr they are likely to be able to leverage this in order to execute code. |
Property Injection | Client | The attacker pollutes properties that the codebase relies on for their informative value, including security properties such as cookies or tokens. For example: if a codebase checks privileges for someuser.isAdmin , then when the attacker pollutes Object.prototype.isAdmin and sets it to equal true , they can then achieve admin privileges. |
Affected environments
The following environments are susceptible to a Prototype Pollution attack:
Application server
Web server
Web browser
How to prevent
Freeze the prototype— use
Object.freeze (Object.prototype)
.Require schema validation of JSON input.
Avoid using unsafe recursive merge functions.
Consider using objects without prototypes (for example,
Object.create(null)
), breaking the prototype chain and preventing pollution.As a best practice use
Map
instead ofObject
.
For more information on this vulnerability type:
Arteau, Oliver. “JavaScript prototype pollution attack in NodeJS application.” GitHub, 26 May 2018
Remediation
Upgrade handlebars
to version 4.7.7 or higher.
References
medium severity
- Vulnerable module: minimist
- Introduced through: handlebars@4.0.14 and html-to-text@3.2.0
Detailed paths
-
Introduced through: hapi-rethinkdb-dash@fs-opensource/hapi-rethinkdb-dash#bde4f9de489cd0cdc2a4ebfa78de82a44b15e1f9 › handlebars@4.0.14 › optimist@0.6.1 › minimist@0.0.10
-
Introduced through: hapi-rethinkdb-dash@fs-opensource/hapi-rethinkdb-dash#bde4f9de489cd0cdc2a4ebfa78de82a44b15e1f9 › html-to-text@3.2.0 › optimist@0.6.1 › minimist@0.0.10
Overview
minimist is a parse argument options module.
Affected versions of this package are vulnerable to Prototype Pollution. The library could be tricked into adding or modifying properties of Object.prototype
using a constructor
or __proto__
payload.
PoC by Snyk
require('minimist')('--__proto__.injected0 value0'.split(' '));
console.log(({}).injected0 === 'value0'); // true
require('minimist')('--constructor.prototype.injected1 value1'.split(' '));
console.log(({}).injected1 === 'value1'); // true
Details
Prototype Pollution is a vulnerability affecting JavaScript. Prototype Pollution refers to the ability to inject properties into existing JavaScript language construct prototypes, such as objects. JavaScript allows all Object attributes to be altered, including their magical attributes such as __proto__
, constructor
and prototype
. An attacker manipulates these attributes to overwrite, or pollute, a JavaScript application object prototype of the base object by injecting other values. Properties on the Object.prototype
are then inherited by all the JavaScript objects through the prototype chain. When that happens, this leads to either denial of service by triggering JavaScript exceptions, or it tampers with the application source code to force the code path that the attacker injects, thereby leading to remote code execution.
There are two main ways in which the pollution of prototypes occurs:
Unsafe
Object
recursive mergeProperty definition by path
Unsafe Object recursive merge
The logic of a vulnerable recursive merge function follows the following high-level model:
merge (target, source)
foreach property of source
if property exists and is an object on both the target and the source
merge(target[property], source[property])
else
target[property] = source[property]
When the source object contains a property named __proto__
defined with Object.defineProperty()
, the condition that checks if the property exists and is an object on both the target and the source passes and the merge recurses with the target, being the prototype of Object
and the source of Object
as defined by the attacker. Properties are then copied on the Object
prototype.
Clone operations are a special sub-class of unsafe recursive merges, which occur when a recursive merge is conducted on an empty object: merge({},source)
.
lodash
and Hoek
are examples of libraries susceptible to recursive merge attacks.
Property definition by path
There are a few JavaScript libraries that use an API to define property values on an object based on a given path. The function that is generally affected contains this signature: theFunction(object, path, value)
If the attacker can control the value of “path”, they can set this value to __proto__.myValue
. myValue
is then assigned to the prototype of the class of the object.
Types of attacks
There are a few methods by which Prototype Pollution can be manipulated:
Type | Origin | Short description |
---|---|---|
Denial of service (DoS) | Client | This is the most likely attack. DoS occurs when Object holds generic functions that are implicitly called for various operations (for example, toString and valueOf ). The attacker pollutes Object.prototype.someattr and alters its state to an unexpected value such as Int or Object . In this case, the code fails and is likely to cause a denial of service. For example: if an attacker pollutes Object.prototype.toString by defining it as an integer, if the codebase at any point was reliant on someobject.toString() it would fail. |
Remote Code Execution | Client | Remote code execution is generally only possible in cases where the codebase evaluates a specific attribute of an object, and then executes that evaluation. For example: eval(someobject.someattr) . In this case, if the attacker pollutes Object.prototype.someattr they are likely to be able to leverage this in order to execute code. |
Property Injection | Client | The attacker pollutes properties that the codebase relies on for their informative value, including security properties such as cookies or tokens. For example: if a codebase checks privileges for someuser.isAdmin , then when the attacker pollutes Object.prototype.isAdmin and sets it to equal true , they can then achieve admin privileges. |
Affected environments
The following environments are susceptible to a Prototype Pollution attack:
Application server
Web server
Web browser
How to prevent
Freeze the prototype— use
Object.freeze (Object.prototype)
.Require schema validation of JSON input.
Avoid using unsafe recursive merge functions.
Consider using objects without prototypes (for example,
Object.create(null)
), breaking the prototype chain and preventing pollution.As a best practice use
Map
instead ofObject
.
For more information on this vulnerability type:
Arteau, Oliver. “JavaScript prototype pollution attack in NodeJS application.” GitHub, 26 May 2018
Remediation
Upgrade minimist
to version 0.2.1, 1.2.3 or higher.
References
medium severity
- Vulnerable module: nodemailer
- Introduced through: nodemailer@3.1.5
Detailed paths
-
Introduced through: hapi-rethinkdb-dash@fs-opensource/hapi-rethinkdb-dash#bde4f9de489cd0cdc2a4ebfa78de82a44b15e1f9 › nodemailer@3.1.5Remediation: Upgrade to nodemailer@6.9.9.
Overview
nodemailer is an Easy as cake e-mail sending from your Node.js applications
Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS) via the attachDataUrls
parameter or when parsing attachments with an embedded file. An attacker can exploit this vulnerability by sending a specially crafted email that triggers inefficient regular expression evaluation, leading to excessive consumption of CPU resources.
Details
Denial of Service (DoS) describes a family of attacks, all aimed at making a system inaccessible to its original and legitimate users. There are many types of DoS attacks, ranging from trying to clog the network pipes to the system by generating a large volume of traffic from many machines (a Distributed Denial of Service - DDoS - attack) to sending crafted requests that cause a system to crash or take a disproportional amount of time to process.
The Regular expression Denial of Service (ReDoS) is a type of Denial of Service attack. Regular expressions are incredibly powerful, but they aren't very intuitive and can ultimately end up making it easy for attackers to take your site down.
Let’s take the following regular expression as an example:
regex = /A(B|C+)+D/
This regular expression accomplishes the following:
A
The string must start with the letter 'A'(B|C+)+
The string must then follow the letter A with either the letter 'B' or some number of occurrences of the letter 'C' (the+
matches one or more times). The+
at the end of this section states that we can look for one or more matches of this section.D
Finally, we ensure this section of the string ends with a 'D'
The expression would match inputs such as ABBD
, ABCCCCD
, ABCBCCCD
and ACCCCCD
It most cases, it doesn't take very long for a regex engine to find a match:
$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCD")'
0.04s user 0.01s system 95% cpu 0.052 total
$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCX")'
1.79s user 0.02s system 99% cpu 1.812 total
The entire process of testing it against a 30 characters long string takes around ~52ms. But when given an invalid string, it takes nearly two seconds to complete the test, over ten times as long as it took to test a valid string. The dramatic difference is due to the way regular expressions get evaluated.
Most Regex engines will work very similarly (with minor differences). The engine will match the first possible way to accept the current character and proceed to the next one. If it then fails to match the next one, it will backtrack and see if there was another way to digest the previous character. If it goes too far down the rabbit hole only to find out the string doesn’t match in the end, and if many characters have multiple valid regex paths, the number of backtracking steps can become very large, resulting in what is known as catastrophic backtracking.
Let's look at how our expression runs into this problem, using a shorter string: "ACCCX". While it seems fairly straightforward, there are still four different ways that the engine could match those three C's:
- CCC
- CC+C
- C+CC
- C+C+C.
The engine has to try each of those combinations to see if any of them potentially match against the expression. When you combine that with the other steps the engine must take, we can use RegEx 101 debugger to see the engine has to take a total of 38 steps before it can determine the string doesn't match.
From there, the number of steps the engine must use to validate a string just continues to grow.
String | Number of C's | Number of steps |
---|---|---|
ACCCX | 3 | 38 |
ACCCCX | 4 | 71 |
ACCCCCX | 5 | 136 |
ACCCCCCCCCCCCCCX | 14 | 65,553 |
By the time the string includes 14 C's, the engine has to take over 65,000 steps just to see if the string is valid. These extreme situations can cause them to work very slowly (exponentially related to input size, as shown above), allowing an attacker to exploit this and can cause the service to excessively consume CPU, resulting in a Denial of Service.
Remediation
Upgrade nodemailer
to version 6.9.9 or higher.
References
medium severity
- Vulnerable module: validator
- Introduced through: thinky@2.3.9
Detailed paths
-
Introduced through: hapi-rethinkdb-dash@fs-opensource/hapi-rethinkdb-dash#bde4f9de489cd0cdc2a4ebfa78de82a44b15e1f9 › thinky@2.3.9 › validator@3.34.0
-
Introduced through: hapi-rethinkdb-dash@fs-opensource/hapi-rethinkdb-dash#bde4f9de489cd0cdc2a4ebfa78de82a44b15e1f9 › thinky@2.3.9 › validator@3.34.0
Overview
validator
is a library of string validators and sanitizers.
Affected versions of this package are vulnerable to Buffer Overflow. It used a regular expression (/^(?:[A-Z0-9+\/]{4})*(?:[A-Z0-9+\/]{2}==|[A-Z0-9+\/]{3}=|[A-Z0-9+\/]{4})$/i
) in order to validate Base64 strings.
Remediation
Upgrade validator
to version 5.0.0 or higher.
References
medium severity
- Vulnerable module: validator
- Introduced through: thinky@2.3.9 and validator@7.0.0
Detailed paths
-
Introduced through: hapi-rethinkdb-dash@fs-opensource/hapi-rethinkdb-dash#bde4f9de489cd0cdc2a4ebfa78de82a44b15e1f9 › thinky@2.3.9 › validator@3.34.0
-
Introduced through: hapi-rethinkdb-dash@fs-opensource/hapi-rethinkdb-dash#bde4f9de489cd0cdc2a4ebfa78de82a44b15e1f9 › validator@7.0.0Remediation: Upgrade to validator@13.6.0.
Overview
validator is a library of string validators and sanitizers.
Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS) via the isSlug
function
PoC
var validator = require("validator")
function build_attack(n) {
var ret = "111"
for (var i = 0; i < n; i++) {
ret += "a"
}
return ret+"_";
}
for(var i = 1; i <= 50000; i++) {
if (i % 10000 == 0) {
var time = Date.now();
var attack_str = build_attack(i)
validator.isSlug(attack_str)
var time_cost = Date.now() - time;
console.log("attack_str.length: " + attack_str.length + ": " + time_cost+" ms")
}
}
Details
Denial of Service (DoS) describes a family of attacks, all aimed at making a system inaccessible to its original and legitimate users. There are many types of DoS attacks, ranging from trying to clog the network pipes to the system by generating a large volume of traffic from many machines (a Distributed Denial of Service - DDoS - attack) to sending crafted requests that cause a system to crash or take a disproportional amount of time to process.
The Regular expression Denial of Service (ReDoS) is a type of Denial of Service attack. Regular expressions are incredibly powerful, but they aren't very intuitive and can ultimately end up making it easy for attackers to take your site down.
Let’s take the following regular expression as an example:
regex = /A(B|C+)+D/
This regular expression accomplishes the following:
A
The string must start with the letter 'A'(B|C+)+
The string must then follow the letter A with either the letter 'B' or some number of occurrences of the letter 'C' (the+
matches one or more times). The+
at the end of this section states that we can look for one or more matches of this section.D
Finally, we ensure this section of the string ends with a 'D'
The expression would match inputs such as ABBD
, ABCCCCD
, ABCBCCCD
and ACCCCCD
It most cases, it doesn't take very long for a regex engine to find a match:
$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCD")'
0.04s user 0.01s system 95% cpu 0.052 total
$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCX")'
1.79s user 0.02s system 99% cpu 1.812 total
The entire process of testing it against a 30 characters long string takes around ~52ms. But when given an invalid string, it takes nearly two seconds to complete the test, over ten times as long as it took to test a valid string. The dramatic difference is due to the way regular expressions get evaluated.
Most Regex engines will work very similarly (with minor differences). The engine will match the first possible way to accept the current character and proceed to the next one. If it then fails to match the next one, it will backtrack and see if there was another way to digest the previous character. If it goes too far down the rabbit hole only to find out the string doesn’t match in the end, and if many characters have multiple valid regex paths, the number of backtracking steps can become very large, resulting in what is known as catastrophic backtracking.
Let's look at how our expression runs into this problem, using a shorter string: "ACCCX". While it seems fairly straightforward, there are still four different ways that the engine could match those three C's:
- CCC
- CC+C
- C+CC
- C+C+C.
The engine has to try each of those combinations to see if any of them potentially match against the expression. When you combine that with the other steps the engine must take, we can use RegEx 101 debugger to see the engine has to take a total of 38 steps before it can determine the string doesn't match.
From there, the number of steps the engine must use to validate a string just continues to grow.
String | Number of C's | Number of steps |
---|---|---|
ACCCX | 3 | 38 |
ACCCCX | 4 | 71 |
ACCCCCX | 5 | 136 |
ACCCCCCCCCCCCCCX | 14 | 65,553 |
By the time the string includes 14 C's, the engine has to take over 65,000 steps just to see if the string is valid. These extreme situations can cause them to work very slowly (exponentially related to input size, as shown above), allowing an attacker to exploit this and can cause the service to excessively consume CPU, resulting in a Denial of Service.
Remediation
Upgrade validator
to version 13.6.0 or higher.
References
medium severity
- Vulnerable module: validator
- Introduced through: thinky@2.3.9 and validator@7.0.0
Detailed paths
-
Introduced through: hapi-rethinkdb-dash@fs-opensource/hapi-rethinkdb-dash#bde4f9de489cd0cdc2a4ebfa78de82a44b15e1f9 › thinky@2.3.9 › validator@3.34.0
-
Introduced through: hapi-rethinkdb-dash@fs-opensource/hapi-rethinkdb-dash#bde4f9de489cd0cdc2a4ebfa78de82a44b15e1f9 › validator@7.0.0Remediation: Upgrade to validator@13.6.0.
Overview
validator is a library of string validators and sanitizers.
Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS) via the isHSL
function.
PoC
var validator = require("validator")
function build_attack(n) {
var ret = "hsla(0"
for (var i = 0; i < n; i++) {
ret += " "
}
return ret+"◎";
}
for(var i = 1; i <= 50000; i++) {
if (i % 1000 == 0) {
var time = Date.now();
var attack_str = build_attack(i)
validator.isHSL(attack_str)
var time_cost = Date.now() - time;
console.log("attack_str.length: " + attack_str.length + ": " + time_cost+" ms")
}
}
Details
Denial of Service (DoS) describes a family of attacks, all aimed at making a system inaccessible to its original and legitimate users. There are many types of DoS attacks, ranging from trying to clog the network pipes to the system by generating a large volume of traffic from many machines (a Distributed Denial of Service - DDoS - attack) to sending crafted requests that cause a system to crash or take a disproportional amount of time to process.
The Regular expression Denial of Service (ReDoS) is a type of Denial of Service attack. Regular expressions are incredibly powerful, but they aren't very intuitive and can ultimately end up making it easy for attackers to take your site down.
Let’s take the following regular expression as an example:
regex = /A(B|C+)+D/
This regular expression accomplishes the following:
A
The string must start with the letter 'A'(B|C+)+
The string must then follow the letter A with either the letter 'B' or some number of occurrences of the letter 'C' (the+
matches one or more times). The+
at the end of this section states that we can look for one or more matches of this section.D
Finally, we ensure this section of the string ends with a 'D'
The expression would match inputs such as ABBD
, ABCCCCD
, ABCBCCCD
and ACCCCCD
It most cases, it doesn't take very long for a regex engine to find a match:
$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCD")'
0.04s user 0.01s system 95% cpu 0.052 total
$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCX")'
1.79s user 0.02s system 99% cpu 1.812 total
The entire process of testing it against a 30 characters long string takes around ~52ms. But when given an invalid string, it takes nearly two seconds to complete the test, over ten times as long as it took to test a valid string. The dramatic difference is due to the way regular expressions get evaluated.
Most Regex engines will work very similarly (with minor differences). The engine will match the first possible way to accept the current character and proceed to the next one. If it then fails to match the next one, it will backtrack and see if there was another way to digest the previous character. If it goes too far down the rabbit hole only to find out the string doesn’t match in the end, and if many characters have multiple valid regex paths, the number of backtracking steps can become very large, resulting in what is known as catastrophic backtracking.
Let's look at how our expression runs into this problem, using a shorter string: "ACCCX". While it seems fairly straightforward, there are still four different ways that the engine could match those three C's:
- CCC
- CC+C
- C+CC
- C+C+C.
The engine has to try each of those combinations to see if any of them potentially match against the expression. When you combine that with the other steps the engine must take, we can use RegEx 101 debugger to see the engine has to take a total of 38 steps before it can determine the string doesn't match.
From there, the number of steps the engine must use to validate a string just continues to grow.
String | Number of C's | Number of steps |
---|---|---|
ACCCX | 3 | 38 |
ACCCCX | 4 | 71 |
ACCCCCX | 5 | 136 |
ACCCCCCCCCCCCCCX | 14 | 65,553 |
By the time the string includes 14 C's, the engine has to take over 65,000 steps just to see if the string is valid. These extreme situations can cause them to work very slowly (exponentially related to input size, as shown above), allowing an attacker to exploit this and can cause the service to excessively consume CPU, resulting in a Denial of Service.
Remediation
Upgrade validator
to version 13.6.0 or higher.
References
medium severity
- Vulnerable module: validator
- Introduced through: thinky@2.3.9 and validator@7.0.0
Detailed paths
-
Introduced through: hapi-rethinkdb-dash@fs-opensource/hapi-rethinkdb-dash#bde4f9de489cd0cdc2a4ebfa78de82a44b15e1f9 › thinky@2.3.9 › validator@3.34.0
-
Introduced through: hapi-rethinkdb-dash@fs-opensource/hapi-rethinkdb-dash#bde4f9de489cd0cdc2a4ebfa78de82a44b15e1f9 › validator@7.0.0Remediation: Upgrade to validator@13.6.0.
Overview
validator is a library of string validators and sanitizers.
Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS) via the isEmail
function.
PoC
var validator = require("validator")
function build_attack(n) {
var ret = ""
for (var i = 0; i < n; i++) {
ret += "<"
}
return ret+"";
}
for(var i = 1; i <= 50000; i++) {
if (i % 10000 == 0) {
var time = Date.now();
var attack_str = build_attack(i)
validator.isEmail(attack_str,{ allow_display_name: true })
var time_cost = Date.now() - time;
console.log("attack_str.length: " + attack_str.length + ": " + time_cost+" ms")
}
}
Details
Denial of Service (DoS) describes a family of attacks, all aimed at making a system inaccessible to its original and legitimate users. There are many types of DoS attacks, ranging from trying to clog the network pipes to the system by generating a large volume of traffic from many machines (a Distributed Denial of Service - DDoS - attack) to sending crafted requests that cause a system to crash or take a disproportional amount of time to process.
The Regular expression Denial of Service (ReDoS) is a type of Denial of Service attack. Regular expressions are incredibly powerful, but they aren't very intuitive and can ultimately end up making it easy for attackers to take your site down.
Let’s take the following regular expression as an example:
regex = /A(B|C+)+D/
This regular expression accomplishes the following:
A
The string must start with the letter 'A'(B|C+)+
The string must then follow the letter A with either the letter 'B' or some number of occurrences of the letter 'C' (the+
matches one or more times). The+
at the end of this section states that we can look for one or more matches of this section.D
Finally, we ensure this section of the string ends with a 'D'
The expression would match inputs such as ABBD
, ABCCCCD
, ABCBCCCD
and ACCCCCD
It most cases, it doesn't take very long for a regex engine to find a match:
$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCD")'
0.04s user 0.01s system 95% cpu 0.052 total
$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCX")'
1.79s user 0.02s system 99% cpu 1.812 total
The entire process of testing it against a 30 characters long string takes around ~52ms. But when given an invalid string, it takes nearly two seconds to complete the test, over ten times as long as it took to test a valid string. The dramatic difference is due to the way regular expressions get evaluated.
Most Regex engines will work very similarly (with minor differences). The engine will match the first possible way to accept the current character and proceed to the next one. If it then fails to match the next one, it will backtrack and see if there was another way to digest the previous character. If it goes too far down the rabbit hole only to find out the string doesn’t match in the end, and if many characters have multiple valid regex paths, the number of backtracking steps can become very large, resulting in what is known as catastrophic backtracking.
Let's look at how our expression runs into this problem, using a shorter string: "ACCCX". While it seems fairly straightforward, there are still four different ways that the engine could match those three C's:
- CCC
- CC+C
- C+CC
- C+C+C.
The engine has to try each of those combinations to see if any of them potentially match against the expression. When you combine that with the other steps the engine must take, we can use RegEx 101 debugger to see the engine has to take a total of 38 steps before it can determine the string doesn't match.
From there, the number of steps the engine must use to validate a string just continues to grow.
String | Number of C's | Number of steps |
---|---|---|
ACCCX | 3 | 38 |
ACCCCX | 4 | 71 |
ACCCCCX | 5 | 136 |
ACCCCCCCCCCCCCCX | 14 | 65,553 |
By the time the string includes 14 C's, the engine has to take over 65,000 steps just to see if the string is valid. These extreme situations can cause them to work very slowly (exponentially related to input size, as shown above), allowing an attacker to exploit this and can cause the service to excessively consume CPU, resulting in a Denial of Service.
Remediation
Upgrade validator
to version 13.6.0 or higher.
References
low severity
- Vulnerable module: minimist
- Introduced through: handlebars@4.0.14 and html-to-text@3.2.0
Detailed paths
-
Introduced through: hapi-rethinkdb-dash@fs-opensource/hapi-rethinkdb-dash#bde4f9de489cd0cdc2a4ebfa78de82a44b15e1f9 › handlebars@4.0.14 › optimist@0.6.1 › minimist@0.0.10
-
Introduced through: hapi-rethinkdb-dash@fs-opensource/hapi-rethinkdb-dash#bde4f9de489cd0cdc2a4ebfa78de82a44b15e1f9 › html-to-text@3.2.0 › optimist@0.6.1 › minimist@0.0.10
Overview
minimist is a parse argument options module.
Affected versions of this package are vulnerable to Prototype Pollution due to a missing handler to Function.prototype
.
Notes:
This vulnerability is a bypass to CVE-2020-7598
The reason for the different CVSS between CVE-2021-44906 to CVE-2020-7598, is that CVE-2020-7598 can pollute objects, while CVE-2021-44906 can pollute only function.
PoC by Snyk
require('minimist')('--_.constructor.constructor.prototype.foo bar'.split(' '));
console.log((function(){}).foo); // bar
Details
Prototype Pollution is a vulnerability affecting JavaScript. Prototype Pollution refers to the ability to inject properties into existing JavaScript language construct prototypes, such as objects. JavaScript allows all Object attributes to be altered, including their magical attributes such as __proto__
, constructor
and prototype
. An attacker manipulates these attributes to overwrite, or pollute, a JavaScript application object prototype of the base object by injecting other values. Properties on the Object.prototype
are then inherited by all the JavaScript objects through the prototype chain. When that happens, this leads to either denial of service by triggering JavaScript exceptions, or it tampers with the application source code to force the code path that the attacker injects, thereby leading to remote code execution.
There are two main ways in which the pollution of prototypes occurs:
Unsafe
Object
recursive mergeProperty definition by path
Unsafe Object recursive merge
The logic of a vulnerable recursive merge function follows the following high-level model:
merge (target, source)
foreach property of source
if property exists and is an object on both the target and the source
merge(target[property], source[property])
else
target[property] = source[property]
When the source object contains a property named __proto__
defined with Object.defineProperty()
, the condition that checks if the property exists and is an object on both the target and the source passes and the merge recurses with the target, being the prototype of Object
and the source of Object
as defined by the attacker. Properties are then copied on the Object
prototype.
Clone operations are a special sub-class of unsafe recursive merges, which occur when a recursive merge is conducted on an empty object: merge({},source)
.
lodash
and Hoek
are examples of libraries susceptible to recursive merge attacks.
Property definition by path
There are a few JavaScript libraries that use an API to define property values on an object based on a given path. The function that is generally affected contains this signature: theFunction(object, path, value)
If the attacker can control the value of “path”, they can set this value to __proto__.myValue
. myValue
is then assigned to the prototype of the class of the object.
Types of attacks
There are a few methods by which Prototype Pollution can be manipulated:
Type | Origin | Short description |
---|---|---|
Denial of service (DoS) | Client | This is the most likely attack. DoS occurs when Object holds generic functions that are implicitly called for various operations (for example, toString and valueOf ). The attacker pollutes Object.prototype.someattr and alters its state to an unexpected value such as Int or Object . In this case, the code fails and is likely to cause a denial of service. For example: if an attacker pollutes Object.prototype.toString by defining it as an integer, if the codebase at any point was reliant on someobject.toString() it would fail. |
Remote Code Execution | Client | Remote code execution is generally only possible in cases where the codebase evaluates a specific attribute of an object, and then executes that evaluation. For example: eval(someobject.someattr) . In this case, if the attacker pollutes Object.prototype.someattr they are likely to be able to leverage this in order to execute code. |
Property Injection | Client | The attacker pollutes properties that the codebase relies on for their informative value, including security properties such as cookies or tokens. For example: if a codebase checks privileges for someuser.isAdmin , then when the attacker pollutes Object.prototype.isAdmin and sets it to equal true , they can then achieve admin privileges. |
Affected environments
The following environments are susceptible to a Prototype Pollution attack:
Application server
Web server
Web browser
How to prevent
Freeze the prototype— use
Object.freeze (Object.prototype)
.Require schema validation of JSON input.
Avoid using unsafe recursive merge functions.
Consider using objects without prototypes (for example,
Object.create(null)
), breaking the prototype chain and preventing pollution.As a best practice use
Map
instead ofObject
.
For more information on this vulnerability type:
Arteau, Oliver. “JavaScript prototype pollution attack in NodeJS application.” GitHub, 26 May 2018
Remediation
Upgrade minimist
to version 0.2.4, 1.2.6 or higher.
References
low severity
- Vulnerable module: tar
- Introduced through: bcrypt@1.0.3
Detailed paths
-
Introduced through: hapi-rethinkdb-dash@fs-opensource/hapi-rethinkdb-dash#bde4f9de489cd0cdc2a4ebfa78de82a44b15e1f9 › bcrypt@1.0.3 › node-pre-gyp@0.6.36 › tar@2.2.2Remediation: Upgrade to bcrypt@2.0.0.
-
Introduced through: hapi-rethinkdb-dash@fs-opensource/hapi-rethinkdb-dash#bde4f9de489cd0cdc2a4ebfa78de82a44b15e1f9 › bcrypt@1.0.3 › node-pre-gyp@0.6.36 › tar-pack@3.4.1 › tar@2.2.2
Overview
tar is a full-featured Tar for Node.js.
Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS). When stripping the trailing slash from files
arguments, the f.replace(/\/+$/, '')
performance of this function can exponentially degrade when f
contains many /
characters resulting in ReDoS.
This vulnerability is not likely to be exploitable as it requires that the untrusted input is being passed into the tar.extract()
or tar.list()
array of entries to parse/extract, which would be unusual.
Details
Denial of Service (DoS) describes a family of attacks, all aimed at making a system inaccessible to its original and legitimate users. There are many types of DoS attacks, ranging from trying to clog the network pipes to the system by generating a large volume of traffic from many machines (a Distributed Denial of Service - DDoS - attack) to sending crafted requests that cause a system to crash or take a disproportional amount of time to process.
The Regular expression Denial of Service (ReDoS) is a type of Denial of Service attack. Regular expressions are incredibly powerful, but they aren't very intuitive and can ultimately end up making it easy for attackers to take your site down.
Let’s take the following regular expression as an example:
regex = /A(B|C+)+D/
This regular expression accomplishes the following:
A
The string must start with the letter 'A'(B|C+)+
The string must then follow the letter A with either the letter 'B' or some number of occurrences of the letter 'C' (the+
matches one or more times). The+
at the end of this section states that we can look for one or more matches of this section.D
Finally, we ensure this section of the string ends with a 'D'
The expression would match inputs such as ABBD
, ABCCCCD
, ABCBCCCD
and ACCCCCD
It most cases, it doesn't take very long for a regex engine to find a match:
$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCD")'
0.04s user 0.01s system 95% cpu 0.052 total
$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCX")'
1.79s user 0.02s system 99% cpu 1.812 total
The entire process of testing it against a 30 characters long string takes around ~52ms. But when given an invalid string, it takes nearly two seconds to complete the test, over ten times as long as it took to test a valid string. The dramatic difference is due to the way regular expressions get evaluated.
Most Regex engines will work very similarly (with minor differences). The engine will match the first possible way to accept the current character and proceed to the next one. If it then fails to match the next one, it will backtrack and see if there was another way to digest the previous character. If it goes too far down the rabbit hole only to find out the string doesn’t match in the end, and if many characters have multiple valid regex paths, the number of backtracking steps can become very large, resulting in what is known as catastrophic backtracking.
Let's look at how our expression runs into this problem, using a shorter string: "ACCCX". While it seems fairly straightforward, there are still four different ways that the engine could match those three C's:
- CCC
- CC+C
- C+CC
- C+C+C.
The engine has to try each of those combinations to see if any of them potentially match against the expression. When you combine that with the other steps the engine must take, we can use RegEx 101 debugger to see the engine has to take a total of 38 steps before it can determine the string doesn't match.
From there, the number of steps the engine must use to validate a string just continues to grow.
String | Number of C's | Number of steps |
---|---|---|
ACCCX | 3 | 38 |
ACCCCX | 4 | 71 |
ACCCCCX | 5 | 136 |
ACCCCCCCCCCCCCCX | 14 | 65,553 |
By the time the string includes 14 C's, the engine has to take over 65,000 steps just to see if the string is valid. These extreme situations can cause them to work very slowly (exponentially related to input size, as shown above), allowing an attacker to exploit this and can cause the service to excessively consume CPU, resulting in a Denial of Service.
Remediation
Upgrade tar
to version 6.1.4, 5.0.8, 4.4.16 or higher.
References
low severity
- Vulnerable module: validator
- Introduced through: validator@7.0.0
Detailed paths
-
Introduced through: hapi-rethinkdb-dash@fs-opensource/hapi-rethinkdb-dash#bde4f9de489cd0cdc2a4ebfa78de82a44b15e1f9 › validator@7.0.0Remediation: Upgrade to validator@9.4.1.
-
Introduced through: hapi-rethinkdb-dash@fs-opensource/hapi-rethinkdb-dash#bde4f9de489cd0cdc2a4ebfa78de82a44b15e1f9 › validator@7.0.0Remediation: Upgrade to validator@9.4.1.
Overview
validator is a library of string validators and sanitizers.
Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS). It used a regular expression (^\s*data:([a-z]+\/[a-z0-9\-\+]+(;[a-z\-]+=[a-z0-9\-]+)?)?(;base64)?,[a-z0-9!\$&',\(\)\*\+,;=\-\._~:@\/\?%\s]*\s*$
) in order to validate Data URIs. This can cause an impact of about 10 seconds matching time for data 70K characters long.
Disclosure Timeline
- Feb 15th, 2018 - Initial Disclosure to package owner
- Feb 16th, 2018 - Initial Response from package owner
- Feb 18th, 2018 - Fix issued
- Feb 18th, 2018 - Vulnerability published
Details
Denial of Service (DoS) describes a family of attacks, all aimed at making a system inaccessible to its original and legitimate users. There are many types of DoS attacks, ranging from trying to clog the network pipes to the system by generating a large volume of traffic from many machines (a Distributed Denial of Service - DDoS - attack) to sending crafted requests that cause a system to crash or take a disproportional amount of time to process.
The Regular expression Denial of Service (ReDoS) is a type of Denial of Service attack. Regular expressions are incredibly powerful, but they aren't very intuitive and can ultimately end up making it easy for attackers to take your site down.
Let’s take the following regular expression as an example:
regex = /A(B|C+)+D/
This regular expression accomplishes the following:
A
The string must start with the letter 'A'(B|C+)+
The string must then follow the letter A with either the letter 'B' or some number of occurrences of the letter 'C' (the+
matches one or more times). The+
at the end of this section states that we can look for one or more matches of this section.D
Finally, we ensure this section of the string ends with a 'D'
The expression would match inputs such as ABBD
, ABCCCCD
, ABCBCCCD
and ACCCCCD
It most cases, it doesn't take very long for a regex engine to find a match:
$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCD")'
0.04s user 0.01s system 95% cpu 0.052 total
$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCX")'
1.79s user 0.02s system 99% cpu 1.812 total
The entire process of testing it against a 30 characters long string takes around ~52ms. But when given an invalid string, it takes nearly two seconds to complete the test, over ten times as long as it took to test a valid string. The dramatic difference is due to the way regular expressions get evaluated.
Most Regex engines will work very similarly (with minor differences). The engine will match the first possible way to accept the current character and proceed to the next one. If it then fails to match the next one, it will backtrack and see if there was another way to digest the previous character. If it goes too far down the rabbit hole only to find out the string doesn’t match in the end, and if many characters have multiple valid regex paths, the number of backtracking steps can become very large, resulting in what is known as catastrophic backtracking.
Let's look at how our expression runs into this problem, using a shorter string: "ACCCX". While it seems fairly straightforward, there are still four different ways that the engine could match those three C's:
- CCC
- CC+C
- C+CC
- C+C+C.
The engine has to try each of those combinations to see if any of them potentially match against the expression. When you combine that with the other steps the engine must take, we can use RegEx 101 debugger to see the engine has to take a total of 38 steps before it can determine the string doesn't match.
From there, the number of steps the engine must use to validate a string just continues to grow.
String | Number of C's | Number of steps |
---|---|---|
ACCCX | 3 | 38 |
ACCCCX | 4 | 71 |
ACCCCCX | 5 | 136 |
ACCCCCCCCCCCCCCX | 14 | 65,553 |
By the time the string includes 14 C's, the engine has to take over 65,000 steps just to see if the string is valid. These extreme situations can cause them to work very slowly (exponentially related to input size, as shown above), allowing an attacker to exploit this and can cause the service to excessively consume CPU, resulting in a Denial of Service.
Remediation
Upgrade validator
to version 9.4.1 or higher.